CLUSTERING-BASED FUSION SYSTEM FOR SKIN LESION CLASSIFICATION USING OPTIMIZED MARINE PREDATORS ALGORITHM

Abstract

ABSTRACT “CLUSTERING-BASED FUSION SYSTEM FOR SKIN LESION CLASSIFICATION USING OPTIMIZED MARINE PREDATORS ALGORITHM” The present invention provides clustering-based fusion system for skin lesion classification using optimized marine predators algorithm that proposes two novel feature fusion strategies, KFS-MPA (using K-means) and DFS-MPA (using DBSCAN), for skin lesion classification. These approaches leverage optimized clustering-based deep feature fusion and the marine predator algorithm (MPA). Ten fused feature sets are evaluated using three classifiers on both datasets, and their performance is compared in terms of dimensionality reduction and accuracy improvement. The results consistently demonstrate that the DFS-MPA approach outperforms KFS-MPA and other compared fusion methods, achieving notable dimensionality reduction and the highest accuracy levels. ROC-AUC curves further support the superiority of DFS-MPA, highlighting its exceptional discriminative capabilities. Five-fold cross-validation tests and a comparison with the previously proposed feature fusion method (FOWFS-AJS) are performed, confirming the effectiveness of DFS-MPA in enhancing classification performance. Figure 1

Specification

Description:TECHNICAL FIELD
[0001] The present invention relates to the field of artificial intelligence and automated systems, and more particularly, the present invention relates to the clustering-based fusion system for skin lesion classification using optimized marine predators algorithm.
BACKGROUND ART
[0002] The following discussion of the background of the invention is intended to facilitate an understanding of the present invention. However, it should be appreciated that the discussion is not an acknowledgment or admission that any of the material referred to was published, known, or part of the common general knowledge in any jurisdiction as of the application's priority date. The details provided herein the background if belongs to any publication is taken only as a reference for describing the problems, in general terminologies or principles or both of science and technology in the associated prior art.
[0003] Skin lesion image classification is vital in dermatology and skin cancer diagnosis due to the increasing prevalence of skin cancer. Early and accurate detection of skin lesions is crucial for timely intervention. Visual inspection alone is subjective and prone to errors, leading to misdiagnosis or delayed treatment. Computer vision and deep learning techniques have advanced automated analysis, providing consistent and objective results. This enables early detection of malignant lesions, reducing workload for dermatologists and improving healthcare efficiency. Accurately classifying skin lesions presents challenges, especially in feature extraction, which is crucial for effective classification models. Skin lesions exhibit high variability in shape, size, colour, and texture, making it difficult to define universal features representing all lesion types. Image artifacts, lighting variations, and low quality distort important features and hinder extraction. Limited annotated training data restricts capturing the full range of lesion characteristics, potentially leading to suboptimal feature representations. Furthermore, the complexity and subtle differences between benign and malignant lesions require informative features for effective discrimination. Overcoming these challenges necessitates advanced techniques capable of handling variability, noise, and data limitations to extract robust and discriminative features for accurate skin lesion classification.
[0004] Deep learning and feature fusion techniques have revolutionized skin lesion classification, providing powerful tools for accurate and automated analysis. Deep learning techniques, such as convolutional neural networks (CNNs), have demonstrated exceptional performance in image classification tasks, including skin lesion analysis. CNNs excel at automatically learning hierarchical representations of image features by leveraging multiple layers of convolutional and pooling operations. These networks can effectively capture both low-level features (e.g., edges, textures) and high-level semantic information (e.g., lesion boundaries, patterns) from skin lesion images. Pre-trained deep learning models, such as VGGNet, ResNet, and Inception, have proven to be beneficial, as they enable transfer learning and leverage large-scale datasets to boost classification accuracy. Feature fusion techniques play a vital role in enhancing the representation power and discriminative capability of skin lesion classification systems. The fusion strategies exploit the strengths of individual features or models, improving classification accuracy, robustness, and generalization capability. By leveraging the strengths of different features, feature fusion enhances the representation power and discriminative capability of the classification models. Feature fusion leverages the complementary nature of different feature sets or modalities to create a more informative representation. By integrating multiple sources of information, feature fusion can capture diverse aspects of skin lesions and improve the classification accuracy. Moreover, the combination of deep learning techniques and feature fusion strategies has propelled skin lesion classification to new heights. These approaches leverage the representation learning capabilities of deep neural networks and the integration of multiple sources of information to achieve more accurate, reliable, and interpretable classification results in the field of dermatology.
[0005] The primary objective of this research is to enhance the accuracy of skin lesion image classification through the development of a comprehensive clustering-based design approach. Building upon our prior work on feature fusion models utilizing the concept of feature-based optimized weighted feature set (FOWFS), this research aims to address certain limitations observed in the previous approach. Specifically, the fixed number of optimized weights and the weight threshold of 0.5 may restrict the adaptability of the model and lead to the exclusion of informative features or the inclusion of less discriminative ones, potentially overlapping with redundant features. To overcome these limitations, this current research introduces an optimized cluster-based feature fusion approach, allowing for flexibility in weight selection and justifying weight thresholds. By incorporating these improvements, the research seeks to enhance the reliability and practical utility of the model in skin lesion classification tasks.
[0006] The study's key contributions revolve around the clustering-based design approach and optimized deep feature fusion. Motivated by the goal of improving understanding and organization of skin lesion patterns, the clustering-based design approach enhances representation and discrimination of different lesion types, ultimately leading to enhanced classification accuracy. By incorporating clustering algorithms, it captures the inherent variability and complexity of skin lesions. Additionally, the integration of deep feature fusion harnesses the power of multiple pre-trained CNN models, extracting and fusing diverse and complementary information from different layers. This significantly boosts the discriminative capability of the classification system, effectively capturing both low-level and high-level features for a comprehensive representation of skin lesions. The study's contributions can be summarized as follows: (a) introducing a comprehensive clustering-based design approach that enhances organization and understanding of skin lesion patterns, improving representation and discrimination; (b) integrating deep feature fusion techniques, leveraging multiple pre-trained models to extract and fuse diverse information; (c) introducing an optimized mechanism for feature fusion, enhancing adaptability and robustness across datasets and varying feature importance; (d) achieving enhanced classification accuracy by addressing the challenges posed by complex and variable skin lesions, aiding in early detection and improved patient outcomes.
[0007] The manuscript is structured as follows: Section 2 presents a comprehensive literature survey, discussing relevant prior research in the field. In Section 3, the methodology adopted in this study is outlined, accompanied by a detailed architectural representation of the proposed work. The experimental setup is described in Section 4, while Section 5 presents a thorough analysis of the obtained results. Section 6 provides an interpretation and in-depth analysis of the findings. The manuscript concludes with Section 7, which summarizes the key outcomes of the research and discusses potential future directions for further investigation.
[0008] In light of the foregoing, there is a need for Clustering-based fusion system for skin lesion classification using optimized marine predators algorithm that overcomes problems prevalent in the prior art associated with the traditionally available method or system, of the above-mentioned inventions that can be used with the presented disclosed technique with or without modification.
[0009] All publications herein are incorporated by reference to the same extent as if each individual publication or patent application were specifically and individually indicated to be incorporated by reference. Where a definition or use of a term in an incorporated reference is inconsistent or contrary to the definition of that term provided herein, the definition of that term provided herein applies, and the definition of that term in the reference does not apply.
OBJECTS OF THE INVENTION
[0010] The principal object of the present invention is to overcome the disadvantages of the prior art by providing clustering-based fusion system for skin lesion classification using optimized marine predators algorithm.
[0011] Another object of the present invention is to provide clustering-based fusion system for skin lesion classification using optimized marine predators algorithm that addresses limitations observed in prior feature fusion models and presents a novel optimization mechanism to improve adaptability and robustness.
[0012] Another object of the present invention is to provide clustering-based fusion system for skin lesion classification using optimized marine predators algorithm that significantly enhances the representation and discrimination of different skin lesion patterns, leading to improved classification accuracy.
[0013] Another object of the present invention is to provide clustering-based fusion system for skin lesion classification using optimized marine predators algorithm that provides a more effective organization and understanding of skin lesion patterns, enhancing representation and discrimination capabilities.
[0014] Another object of the present invention is to provide clustering-based fusion system for skin lesion classification using optimized marine predators algorithm wherein, the integration of deep feature fusion leverages the power of multiple pre-trained CNN models, extracting diverse and complementary information for comprehensive representation.
[0015] Another object of the present invention is to provide clustering-based fusion system for skin lesion classification using optimized marine predators algorithm that ensures adaptability across datasets and varying feature importance, enhancing classification accuracy.
[0016] Another object of the present invention is to provide clustering-based fusion system for skin lesion classification using optimized marine predators algorithm that addresses the challenges posed by complex and variable skin lesions, which can aid in early detection and improved patient outcomes.
[0017] Another object of the present invention is to provide clustering-based fusion system for skin lesion classification using optimized marine predators algorithm that lies in the significant improvements achieved in skin lesion image classification accuracy, supported by the superior performance of the DFS-MPA approach over other fusion methods.
[0018] The foregoing and other objects of the present invention will become readily apparent upon further review of the following detailed description of the embodiments as illustrated in the accompanying drawings.
SUMMARY OF THE INVENTION
[0019] The present invention relates to clustering-based fusion system for skin lesion classification using optimized marine predators algorithm.
[0020] This manuscript presents a comprehensive approach to enhance the accuracy of skin lesion image classification based on the HAM10000 and BCN20000 datasets. Building on prior feature fusion models, this research introduces an optimized cluster-based fusion approach to address limitations observed in our previous methods. The study proposes two novel feature fusion strategies, KFS-MPA (using K-means) and DFS-MPA (using DBSCAN), for skin lesion classification. These approaches leverage optimized clustering-based deep feature fusion and the marine predator algorithm (MPA). Ten fused feature sets are evaluated using three classifiers on both datasets, and their performance is compared in terms of dimensionality reduction and accuracy improvement. The results consistently demonstrate that the DFS-MPA approach outperforms KFS-MPA and other compared fusion methods, achieving notable dimensionality reduction and the highest accuracy levels. ROC-AUC curves further support the superiority of DFS-MPA, highlighting its exceptional discriminative capabilities. Five-fold cross-validation tests and a comparison with the previously proposed feature fusion method (FOWFS-AJS) are performed, confirming the effectiveness of DFS-MPA in enhancing classification performance. The statistical validation based on the Friedman test and Bonferroni-Dunn test also supports DFS-MPA as a promising approach for skin lesion classification among the evaluated feature fusion methods. These findings emphasize the significance of optimized cluster-based deep feature fusion in skin lesion classification and establish DFS-MPA as the preferred choice for feature fusion in this study.
[0021] In conclusion, this research has successfully achieved its primary objective of enhancing skin lesion image classification accuracy through a comprehensive clustering-based deep feature fusion approach. The proposed method addresses limitations observed in prior feature fusion models and presents a novel optimization mechanism to improve adaptability and robustness. The clustering-based design approach, combined with deep feature fusion techniques, significantly enhances the representation and discrimination of different skin lesion patterns, leading to improved classification accuracy. The contributions of this study are multi-fold. Firstly, the clustering-based design approach provides a more effective organization and understanding of skin lesion patterns, enhancing representation and discrimination capabilities. Secondly, the integration of deep feature fusion leverages the power of multiple pre-trained CNN models, extracting diverse and complementary information for comprehensive representation. Thirdly, the optimized mechanism for feature fusion ensures adaptability across datasets and varying feature importance, enhancing classification accuracy. Lastly, the research addresses the challenges posed by complex and variable skin lesions, which can aid in early detection and improved patient outcomes. The impact of this research lies in the significant improvements achieved in skin lesion image classification accuracy, supported by the superior performance of the DFS-MPA approach over other fusion methods.
[0022] While the invention has been described and shown with reference to the preferred embodiment, it will be apparent that variations might be possible that would fall within the scope of the present invention.
BRIEF DESCRIPTION OF DRAWINGS
[0023] So that the manner in which the above-recited features of the present invention can be understood in detail, a more particular description of the invention, briefly summarized above, may have been referred by embodiments, some of which are illustrated in the appended drawings. It is to be noted, however, that the appended drawings illustrate only typical embodiments of this invention and are therefore not to be considered limiting of its scope, for the invention may admit to other equally effective embodiments.
[0024] These and other features, benefits, and advantages of the present invention will become apparent by reference to the following text figure, with like reference numbers referring to like structures across the views, wherein:
[0025] Figure 1. Cluster-based ooptimized weighted feature set generation processes (a) KFS and; (b) DFS;
[0026] Figure 2. Observed performance of proposed feature sets for HAM 10000 and BCN 20000 datasets with respect to accuracy, sensitivity, specificity and F-score;
[0027] Figure 3. The Average training time (in minutes) for HAM10000 andBCN 20000 datasets;
[0028] Figure 4. Convergence curves optimized versions of KFS and DFS for HAM10000 and BCN20000 datasets;
[0029] Figure 5. Observed performance of DFS-MPA for HAM 10000 and BCN 20000 datasets with respect to accuracy, sensitivity, specificity and F-score; and
[0030] Figure 6. ROC-AUC curves for HAM10000 and BCN20000 datasets.
DETAILED DESCRIPTION OF THE INVENTION
[0031] While the present invention is described herein by way of example using embodiments and illustrative drawings, those skilled in the art will recognize that the invention is not limited to the embodiments of drawing or drawings described and are not intended to represent the scale of the various components. Further, some components that may form a part of the invention may not be illustrated in certain figures, for ease of illustration, and such omissions do not limit the embodiments outlined in any way. It should be understood that the drawings and the detailed description thereto are not intended to limit the invention to the particular form disclosed, but on the contrary, the invention is to cover all modifications, equivalents, and alternatives falling within the scope of the present invention as defined by the appended claim.
[0032] As used throughout this description, the word "may" is used in a permissive sense (i.e. meaning having the potential to), rather than the mandatory sense, (i.e. meaning must). Further, the words "a" or "an" mean "at least one" and the word "plurality" means "one or more" unless otherwise mentioned. Furthermore, the terminology and phraseology used herein are solely used for descriptive purposes and should not be construed as limiting in scope. Language such as "including," "comprising," "having," "containing," or "involving," and variations thereof, is intended to be broad and encompass the subject matter listed thereafter, equivalents, and additional subject matter not recited, and is not intended to exclude other additives, components, integers, or steps. Likewise, the term "comprising" is considered synonymous with the terms "including" or "containing" for applicable legal purposes. Any discussion of documents, acts, materials, devices, articles, and the like are included in the specification solely for the purpose of providing a context for the present invention. It is not suggested or represented that any or all these matters form part of the prior art base or were common general knowledge in the field relevant to the present invention.
[0033] In this disclosure, whenever a composition or an element or a group of elements is preceded with the transitional phrase "comprising", it is understood that we also contemplate the same composition, element, or group of elements with transitional phrases "consisting of", "consisting", "selected from the group of consisting of, "including", or "is" preceding the recitation of the composition, element or group of elements and vice versa.
[0034] The present invention is described hereinafter by various embodiments with reference to the accompanying drawing, wherein reference numerals used in the accompanying drawing correspond to the like elements throughout the description. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiment set forth herein. Rather, the embodiment is provided so that this disclosure will be thorough and complete and will fully convey the scope of the invention to those skilled in the art. In the following detailed description, numeric values and ranges are provided for various aspects of the implementations described. These values and ranges are to be treated as examples only and are not intended to limit the scope of the claims. In addition, several materials are identified as suitable for various facets of the implementations. These materials are to be treated as exemplary and are not intended to limit the scope of the invention.
[0035] The present invention relates to clustering-based fusion system for skin lesion classification using optimized marine predators algorithm.
[0036] This section provides an overview of the previously proposed feature fusion strategies, highlighting their advantages and areas for improvement, which are addressed in this research work. The discussion includes a brief overview of pre-trained networks, feature extraction and fusion techniques, clustering techniques, and the rationale behind their use. Additionally, the marine predator algorithm (MPA) meta-heuristic optimization algorithm and the architecture of the proposed fusion approaches are outlined, offering insights into the methodology employed in this study.
[0037] The previous study explored the effectiveness of inductive transfer learning at the feature level, utilizing three pre-trained CNNs. The results indicated that this approach achieved superior performance compared to traditional feature selection models. By employing feature fusion models, such as CFS, the study successfully merged the outputs of the pre-trained networks, resulting in enhanced classification performance. Furthermore, the investigation conducted a comparative analysis between basic fusion strategies and a weighted approach for feature selection. The experimental findings demonstrated the superiority of the weighted approach, particularly the AWFS method, in terms of performance. The research also emphasized the significance of decision-making within feature fusion methodologies. By leveraging the AJS optimizer, the study identified the optimal point for feature fusion, taking into account both active and passive motions of the algorithm. This strategic approach facilitated the identification of the best cost, ultimately enhancing the overall performance of the system. In addition to the aforementioned contributions, the research introduced two decision-based feature fusion models: the MOWFS and FOWFS approaches. In the model-based approach, the cost function was based on one of the classifiers (DT, NB, MLP, and SVM), and optimized weights were derived from all three pre-trained models. On the other hand, the feature-based strategy optimized weights individually for each feature, resulting in the creation of a combined feature set. These innovative strategies significantly improved the classification performance of the system. In summary, the previous research investigated the advantages of inductive transfer learning, designed robust classifier models, employed feature fusion techniques, compared basic fusion strategies with a weighted approach, highlighted the importance of decision-making in feature fusion, and introduced novel decision-based feature fusion models. However, we observed that in FOWFS, the fixed number of optimized weights and the weight threshold of 0.5 may restrict the adaptability of the model and potentially lead to the exclusion of informative features or the inclusion of less discriminative ones. This could also result in overlaps with redundant features. To overcome these limitations, the present research introduces novel strategies called optimized cluster-based feature fusion utilizing the K-means and density-based clustering DBSCAN clustering algorithms namely K-means based feature set (KFS) and DBSCAN based feature set (DFS) respectively.
[0038] CNNs offer pre-trained models that have undergone extensive training on large-scale image classification datasets. These models can be used as-is or customized to fulfil specific requirements. This technique, known as transfer learning, allows the application of knowledge acquired from one task to a similar but distinct task. The field of image processing benefits immensely from a wide range of pre-trained CNN models, including LeNet, AlexNet, ResNet, GoogleNet (or InceptionNet), VGG, DenseNet, EfficientNet, PolyNet, and others. CNNs are built upon neural networks and incorporate fundamental components like convolution layers, pooling layers, and activation layers. In alignment with previous work, this study focuses on feature selection and devising a feature fusion strategy, utilizing three pre-trained CNNs-VGG16, EfficientNet B0, and ResNet50.
[0039] Pre-trained CNNs such as VGG16, EfficientNet B0, and ResNet50 have established themselves as powerful tools in the realm of computer vision and image processing, finding application in the experimentation conducted within this study. These models have undergone training on vast datasets comprising millions of labelled images, enabling them to learn intricate patterns and extract meaningful features through an iterative optimization process. VGG16, developed by the visual geometry group, boasts simplicity and uniformity, with multiple convolutional layers followed by max-pooling layers. By cascading these layers, VGG16 captures hierarchical features of increasing complexity, culminating in accurate image classification. The fully connected layers act as classifiers, generating predictions based on the learned features. EfficientNet B0, in contrast, employs a compound scaling technique, striking a balance between model size and performance. It combines depth-wise separable convolutions, which reduce computational complexity, with efficient scaling methods to achieve cutting-edge accuracy. EfficientNet B0's architecture enables efficient processing and discriminative feature learning from images. ResNet50 introduced skip connections, or identity shortcuts, which revolutionized deep learning by addressing the issue of vanishing gradients. These skip connections facilitate more effective gradient flow during training, enabling the training of significantly deeper networks. With convolutional layers, batch normalization, and fully connected layers, ResNet50 captures intricate details and robustly recognizes objects and patterns in images. Utilizing pre-trained networks offers various advantages, such as feature extraction by extracting learned representations from intermediate layers, fine-tuning to adapt models to new datasets or domains while preserving pre-trained weights, and transfer learning to leverage knowledge from pre-training for related tasks, resulting in faster convergence and improved performance. By leveraging the knowledge captured by these pre-trained CNNs, we can effectively utilize their learned representations to achieve high-performance outcomes in our specific tasks.
[0040] The K-means clustering algorithm is a popular unsupervised machine learning technique used for partitioning data into distinct clusters. It aims to minimize the within-cluster sum of squares, or distortion, by iteratively assigning data points to the nearest cluster centroid and updating the centroids based on the mean of the assigned points. The algorithm begins with the initialization of centroids, either randomly or using a specific method. It then iterates between two steps: assigning each data point to the nearest centroid and updating the centroids based on the assigned points. The algorithm continues this process until convergence, where the centroids no longer change significantly. K-means clustering is known for its computational efficiency and scalability, making it suitable for handling large datasets. However, it is sensitive to outliers and can be affected by the initial placement of centroids. The choice of the number of clusters ( ) is an important consideration in the algorithm and can be determined using domain knowledge or techniques such as the elbow method.
[0041] Density-based clustering is an effective approach used to identify clusters or groups within a dataset based on the density of data points. Unlike traditional clustering methods that rely on predetermined cluster shapes or distance measures, density-based clustering focuses on the density of data points in their local neighbourhoods. The most widely recognized algorithm for density-based clustering is density-based spatial clustering of applications with noise (DBSCAN). The DBSCAN defines clusters as regions of high density separated by regions of lower density. It classifies data points into three categories: core points, border points, and noise points. Core points have a sufficient number of neighbouring points within a specified radius, while border points have fewer neighbours but are within the radius of a core point. Noise points are isolated points that do not belong to any cluster. One of the key advantages of density-based clustering is its ability to discover clusters of various shapes and effectively handle noise. It is robust against outliers and does not require prior knowledge of the number of clusters. Density-based clustering is also capable of handling datasets with varying cluster densities and is less sensitive to parameter settings compared to distance-based algorithms like K-means. The crucial parameters in density-based clustering algorithms are the radius or epsilon ( ), which defines the distance within which neighbouring points are considered, and the minimum number of points ( ) required to form a core point. These parameters significantly influence the granularity and quality of the resulting clusters. The flexibility and robustness of density-based clustering make it a valuable tool for exploratory data analysis and gaining insights into the underlying structure of complex datasets.
[0042] The primary motivation behind introducing a clustering-based approach lies in mitigating the limitations associated with weight-based models, which often assign high weights to specific features, resulting in redundancy across various feature extraction methodologies. Through the adoption of a clustering-based paradigm, the objective is to tackle this issue by aggregating similar features into clusters within the extracted feature set. This approach ensures comprehensive feature coverage while concurrently reducing the overall feature dimensionality. Here, features are grouped into clusters, and the features within each cluster are averaged to represent that particular group. This streamlined approach prevents the disregard of potentially valuable information. The conceptualization of feature fusion models utilizing this clustering-based methodology draws inspiration from the strengths of both K-means and DBSCAN algorithms. K-means is favoured for its simplicity, efficiency, and interpretability, serving as a fundamental tool for clustering-based feature fusion strategies. It facilitates the amalgamation and extraction of features from multiple sources based on centroid proximity. However, K-means encounters challenges when confronted with datasets containing numerous outliers, thus limiting its efficacy in certain scenarios. To counteract this limitation, the integration of DBSCAN is instrumental. Renowned for its ability to identify clusters of arbitrary shapes and robustly handle outliers, DBSCAN enriches the feature fusion models by incorporating the concept of density-based clustering. This integration empowers the feature fusion process to better discern and encapsulate intricate patterns and variations within the data, thereby enhancing the overall efficacy of the fusion methodology.
[0043] The MPA is a nature-inspired meta-heuristic optimization algorithm that draws inspiration from the hunting behaviour of marine predators in the ocean ecosystem. Developed based on the concept of predator-prey interactions, MPA mimics the hunting strategies employed by marine predators to search for and capture their prey efficiently. The algorithm starts with an initial population of potential solutions, representing the predators in the search space. These predators interact with their prey, which corresponds to the problem's objective function. The movement of predators is guided by various parameters, including their position, speed, and perception of prey. During the search process, predators employ different tactics, such as cruising, searching, and attacking, to locate and capture the prey. These tactics involve a balance between exploration and exploitation, allowing the algorithm to efficiently explore the solution space while converging towards promising regions. The movement of predators is influenced by several factors, including the position and fitness of the prey, the predator's position, and the social behavior of neighboring predators. Through these interactions and information exchanges, the algorithm adapts and refines its search strategy, gradually improving the quality of solutions.
[0044] One of the key advantages of this MPA is its ability to handle complex and multimodal optimization problems. By mimicking the natural hunting behaviour of marine predators, MPA exhibits an inherent ability to balance exploration and exploitation, promoting effective search and convergence. The MPA algorithm has been applied to various optimization problems, including function optimization, feature selection, image segmentation, and parameter tuning for machine learning algorithms. Its performance has shown competitiveness against other popular meta-heuristic algorithms in terms of solution quality and convergence speed. However, like other meta-heuristic algorithms, the performance of MPA is influenced by several parameters, such as population size, movement parameters, and the search space representation. Proper parameter tuning and adaptation are essential for achieving optimal results with MPA. This algorithm provides a promising approach for solving complex optimization problems, leveraging the natural hunting behaviours of marine predators to guide the search process and discover high-quality solutions in various domains.
[0045] The MPA leverages the advantageous characteristics of the Lévy strategy in combination with the features of Brownian motion, which have been proven to enhance the efficiency of exploration and exploitation. In standard Brownian motion, the step length follows a probability function defined by a Normal (Gaussian) distribution with zero mean ( 0) and unit variance ). On the other hand, Lévy flight is a type of random walk where the step sizes are determined based on a probability function. The Lévy strategy predominantly explores the domain with small steps interspersed with occasional long jumps, allowing for effective exploration of remote regions. In contrast, the Brownian motion covers the domain more uniformly and with controlled steps, enabling focused exploitation of specific areas. By incorporating both Lévy flight and Brownian motion, the MPA algorithm benefits from the balanced and complementary advantages of these two strategies, facilitating efficient exploration and exploitation of the solution space. The following are the steps observed during formulation of MPA.
[0046] Like many other meta-heuristics, the MPA follows a population-based approach, where the initial solutions are uniformly distributed across the search space as the initial trial.
[0047] In this MPA, the fittest solution is designated as the top predator and used to construct a matrix known as the matrix inspired by the principle of survival of the fittest. This matrix guides the algorithm in efficiently exploring and locating promising solutions, akin to nature's skilled foragers. The top performers are crucially preserved to maintain the best solutions, preventing the loss of valuable information and guiding the search towards improvements. They impact the exploration-exploitation balance, enhancing population quality and influencing the search behaviour of other individuals.
[0048] The top predator vector, represented by , is replicated times to form the matrix, where denotes the number of search agents and represents the number of dimensions. Notably, both predators and prey are considered as search agents, as prey seeks its own sustenance while predators search for their prey. At the conclusion of each iteration the matrix is updated if a superior predator replaces the top predator.
[0049] The matrix, sharing the same dimensions as the matrix, serves as a reference for the predators to update their positions. In simpler terms, during initialization, the matrix is created, and the fittest individual (predator) constructs the matrix. The matrix is represented as follows, where, represents the dimension of the prey. It is important to emphasize that the entire optimization process is primarily and directly linked to these two matrices.
[0050] The MPA algorithm consists of three primary optimization phases, each corresponding to different velocity ratios that simulate the complete life cycle of both prey and predator.
[0051] Exploration phase: During this phase, the prey exhibits rapid movement and engages in an exploratory behaviour using Brownian motion (BM) [28-29] to search for its food. In contrast, the predator remains stationary, carefully observing the prey's movements. This exploratory phase takes place during the initial third of the iterations as stated in Equation (4). Let be a vector of randomly generated numbers following a Gaussian distribution representing the BM. The and represents entry-wise multiplication and a constant value of 0.5 respectively. The vector contains random numbers uniformly distributed in the range of [0~1] and the variables and denote the current iteration and maximum number of iterations respectively.
[0052] Transitional Phase (bridging the exploration and exploitation phases): In this intermediate Phase, both the prey and predator take nearly equal steps, representing a transition from the exploration phase to the exploitation phase. The prey adopts an exploitative strategy using Lévy flight (LF) [28-29], while the predator continues its exploratory behaviour through BM. The entire population divides into two equal groups, with the first group focused on exploitation and the second group dedicated to exploration using Equation (4) and Equation (5).
[0053] The vector represents randomly generated numbers following the LF distribution, and the prey's movement in Levy fashion is characterized by with the addition of the step size to the prey's position. This step size, derived from the Levy distribution, primarily consists of small steps, making it suitable for exploitation purposes. The prey's motion can be mathematically represented as shown in Equation (5). The behaviour of the second group can be described using Equation (6). In this case, the convergence factor is defined as; , which adaptively adjusts the step size of the predator's motion enabling effective exploration of the search space during the exploration phase. The predator's motion, following Brownian motion, is modelled by the product of , while the prey's positions are updated based on the Brownian motion of the predator.
[0054] (c) Exploitation Phase: During this phase, the predator moves at a higher speed compared to the prey. Employing Lévy flight, the predator executes an exploitative behaviour to capture the prey. This third phase occurs during the final third of the iterations and can be mathematically expressed as mentioned in Equation (7).
[0055] The predator motion following the Levy strategy can be represented as the product . By adding the step size to the position of the top performer ), this model aid in updating the prey's position more effectively during the optimization process.
[0056] Eddy Formation Enhanced by the Influence of Fish Aggregating Devices (FAD)
[0057] In the marine environment, the formation of eddies and the presence of FADs significantly influence the behaviour of marine predators and sharks tend to spend most of their time in close proximity to FADs. However, during the remaining time, they venture into longer skips in various directions to explore areas with diverse prey distribution. This combination of FADs and long skips helps the MPA algorithm avoid stagnation in local optima, thereby enhancing its overall performance. The scenario involving FADs can be modelled as using Equation (8). The FADs denote the probability of FADs effect=0.2, defines the binary vector having arrays compromising 0 and 1. The is uniform random number value introduced earlier and the value is within [0~1] and and represent index of random numbers for matrix.
[0058] Marine predators possess a remarkable memory that enables them to recall effective foraging locations. Similarly, in the MPA algorithm, a memory mechanism is employed to store the optimal solution obtained from earlier iterations. The current solution is then compared to the stored solution, and if the present solution performs better, it replaces the earlier one in the memory.
[0059] In the initial phase of experimentation, following the approach mentioned in the previous work [ ], the original feature sets are utilized as output from the pre-trained models including VGG16 (with 512 number of features), EfficientNet B0 (with 1024 number of features) and ResNet50 (1024 number of features) to the classification algorithms. The choice of fusion techniques is motivated by their ability to leverage the advantages of ensemble approaches, where the combination of results from multiple base models has proven to enhance the performance of final predictions. Additionally, these fusion techniques provide improved robustness by considering the dispersion or spread of predictions and model performance. Aligned with these considerations, this study also places a primary focus on the design of feature fusion strategies that explore the capabilities of pre-trained learning architectures.
[0060] In the previous work, four ensemble feature fusion strategies were proposed: CFS, AWFS, MOWFS, and FOWFS. CFS involves a simple ensemble technique where the outputs of the three pre-trained models are concatenated to form a batch of feature sets. AWFS utilizes adaptive weight selection and concatenation of features from each pre-trained model. MOWFS applies the AJS optimization algorithm to determine optimal weights through model-based cost evaluation. FOWFS focuses on feature-based optimization, resulting in optimized weights and a combined feature set. Features with weights above 0.5 are considered best performing. Additionally, this work explores two more fusion strategies: KFS and DFS, based on two widely used clustering approaches.
[0061] In KFS, the initial step involves combining all the features from the three pre-trained networks to form the feature sets, denoted as, . The optimization process begins by considering the decision variable for K-means clustering, where represents the desired number of clusters to be formed. The aim is to find an optimized value for within the range of 100 to 2000, allowing the formation of -clusters. Subsequently, a new feature set is constructed by considering the average of the data within each cluster. Finally, an artificial neural network (ANN) is employed for the classification task. Similarly, in DWFS, the feature sets are created by combining all the features from the three pre-trained networks, following a similar approach to KFS. The optimization process focuses on optimizing two parameters of the density-based clustering technique: and , which determine the formation of clusters. Subsequently, new feature sets are constructed by taking the average of the data within each cluster. Finally, the classification task is performed using ANN, similar to the approach taken in KFS. Figure 1 (a) and (b) illustrate the operational flow of the newly proposed feature fusion strategies. Additionally, the Algorithm 1 and Algorithm 2 in this section illustrate the operational workflow of the proposed KFS and DFS feature fusion strategies, respectively.
[0062] This section aims to comprehensively depict the experimental stages conducted to elucidate the findings of the study. It encompasses a discussion on the datasets and parameter descriptions employed, as well as the evaluation metrics utilized to assess the performance of the proposed feature fusion approach algorithm. The experimentation was conducted on a system comprising an Intel(R) Core (TM) i5-7200U CPU @ 2.50 GHz with a 2.71 GHz processor, 4.00 GB (3.88 GB usable) RAM, a 64-bit operating system, x64-based processor operating system, and executed on the Google Colab platform.
[0063] Furthermore, in Figure 2, we can observe a comprehensive portrayal of the performance evaluation metrics, namely accuracy, sensitivity, specificity, and F-score (based on ANN), specifically for the feature fusion approaches discussed earlier. This detailed analysis is centred around two significant datasets: HAM10000 and BCN 20000. The visual representation of these metrics leaves no room for doubt, as it unequivocally illustrates the exceptional superiority of the DFS-MPA approach, effectively outshining all the other feature fusion strategies that were compared. Notably, the superiority of DFS-MPA is evident across all the evaluation metrics, establishing its dominance in the realm of feature fusion methods for this particular classification task. Moreover, in Figure 3, we are presented with another crucial aspect of the evaluation process. It showcases the average training time (measured in minutes) for each of the feature fusion strategies considered, once again for both the HAM10000 and BCN 20000 datasets.
[0064] This insightful addition provides valuable information regarding the computational efficiency of the approaches. The combination of Figure 2 and Figure 3 offers a comprehensive and compelling overview of the comparative performance of feature fusion methods, where DFS-MPA not only excels in terms of accuracy, sensitivity, specificity, and F-score but also demonstrates competitive efficiency in terms of training time.
[0065] In order to gain valuable insights into the performance and behaviour of the optimized versions of KFS and DFS during training with the ANN classifier, learning curves were plotted. The results exhibit promising performance for both KFS-MPA and DFS-MPA. Notably, DFS-MPA distinguishes itself by outperforming all other feature fusion approaches, as clearly demonstrated in Figure 4.
[0066] Following the impressive performance of DFS-MPA on ANN, a further evaluation was conducted to assess the classification accuracy along with few other accuracy measures of the proposed DFS-MPA using deep learning-based classifiers, specifically long short-term memory (LSTM) and CNN. The results of this evaluation are presented in Table 3 for both the HAM 10000 and BCN 20000 datasets. Among these models, the DFS-MPA-CNN approach stands out as the highest-performing method for both datasets. For the HAM 10000 dataset, DFS-MPA-CNN achieves an impressive accuracy of 0.9723, which is the highest among all the neural network models. Additionally, it demonstrates high sensitivity (0.9782) and specificity (0.9766), showcasing its ability to accurately identify both positive and negative cases. The F1-Score, which considers the balance between precision and recall, is also notably high at 0.9773, indicating overall robust performance. Similarly, for the BCN 20000 dataset, DFS-MPA-CNN outperforms the other models with an exceptional accuracy score of 0.9802, which is the highest achieved among all the models. Moreover, it exhibits high sensitivity (0.9789) and specificity (0.9794), demonstrating its effectiveness in correctly classifying skin lesion cases. The F1-Score is also remarkably high at 0.9791, further affirming the overall outstanding performance of DFS-MPA-CNN. The recorded results in Table 3 highlight that the DFS-MPA-CNN approach is the most effective and reliable method for skin lesion image classification tasks for both the HAM 10000 and BCN 20000 datasets.
[0067] To gain deeper insights into the proposed DFS-MPA feature fusion approach, its performance in terms of accuracy, sensitivity, specificity, and F-score was specifically evaluated using the CNN classifier. The recorded results are depicted in Figure 5 for both datasets, providing a comprehensive view of how DFS-MPA performs with the CNN classifier for both the datasets.
[0068] In order to facilitate a clear understanding of the evaluation metrics and gain insights into the strengths and weaknesses of the DFS-MPA method for skin lesion image classification tasks, we have included Table 4 to Table 7. These tables present the vital five-fold test results of the DFS-MPA method when utilizing three different classifiers: ANN, LSTM, and CNN. Through these tables, a comprehensive and comparative analysis of the method's performance on two distinct datasets, HAM10000 and BCN20000, is provided, focusing on accuracy, sensitivity, specificity, and F-score. The tabular format allows for concise and transparent presentation of the metrics, enabling readers to readily assess the effectiveness of DFS-MPA with each classifier on both datasets.
[0069] The comprehensive and comparative analysis of the DFS-MPA method's performance across the four tables consistently indicates that the DFS-MPA-CNN classifier stands out as the most effective choice for skin lesion image classification tasks. Its consistent superior performance in accuracy, sensitivity, specificity, and F-score highlights its potential suitability for accurate and reliable skin lesion classification, making it a promising approach for medical image analysis and diagnosis.
[0070] The ROC-AUC curves were generated to take advantage of visualizing the learning activities of the proposed DFS-MPA across all three classifiers (ANN, long-short term memory (LSTM) [ ], and CNN). These curves offer a comprehensive representation of the classifiers' performance by showcasing the trade-off between true positive rates (sensitivity) and false positive rates (1-specificity) at different decision thresholds. The concise summary provided by these ROC-AUC curves allows us to assess the classifiers' ability to distinguish between positive and negative cases, regardless of the threshold chosen. Moreover, the area under the ROC curve (AUC) serves as a singular metric to quantify the overall classifier performance, where higher AUC values indicate better discriminative capabilities. Analyzing the ROC-AUC curves in Figure 5 for both the HAM10000 and BCN20000 datasets provides valuable insights into the effectiveness of the DFS-MPA approach. Notably, the AUC values for CNN classifiers are 0.9759 for HAM10000 and 0.9788 for BCN20000, indicating the superior performance of DFS-MPA in both cases.
[0071] The presented manuscript makes several significant contributions to the field of skin lesion image classification. First, the proposed clustering-based feature fusion approach demonstrates its effectiveness in enhancing the accuracy of classification. Through the fusion of different feature sets, a notable reduction in dimensionality is achieved, improving the efficiency of the classification process. Moreover, the study evaluates ten fused feature sets using three classifiers on two diverse datasets, providing a comprehensive analysis of the proposed method's performance.
[0072] The main impact of the study lies in the specific focus on the DFS-MPA approach, which outperforms other fusion methods consistently in terms of dimensionality reduction and accuracy improvement. DFS-MPA attains impressive results, including a 54.69% reduction in dimensionality for HAM10000 and 50.47% for BCN20000, as well as achieving the highest accuracy levels among all the fused feature sets for both datasets. The superiority of DFS-MPA is further supported by the ROC-AUC curves, illustrating its exceptional discriminative capabilities.
[0073] Additionally, the in-depth evaluations with different classifiers demonstrate the versatility of the DFS-MPA approach, particularly highlighting the exceptional performance of DFS-MPA-CNN, which achieves remarkable accuracy, sensitivity, specificity, and F-score in skin lesion classification scenarios.
[0074] Various modifications to these embodiments are apparent to those skilled in the art from the description and the accompanying drawings. The principles associated with the various embodiments described herein may be applied to other embodiments. Therefore, the description is not intended to be limited to the 5 embodiments shown along with the accompanying drawings but is to be providing the broadest scope consistent with the principles and the novel and inventive features disclosed or suggested herein. Accordingly, the invention is anticipated to hold on to all other such alternatives, modifications, and variations that fall within the scope of the present invention and appended claims. , Claims:CLAIMS
We Claim:
1) A clustering-based feature fusion system for skin lesion classification, the system comprising:
- a feature extraction module configured to utilize a plurality of pre-trained convolutional neural networks (CNNs) to extract feature sets from input skin lesion images;
- a feature fusion module that aggregates feature sets using optimized clustering-based algorithms, including K-means and density-based spatial clustering of applications with noise (DBSCAN), to form fused feature sets;
- a classification module configured to classify the fused feature sets into predefined skin lesion categories.
2) The system as claimed in claim 1, wherein the feature extraction module employs VGG16, EfficientNet B0, and ResNet50 as pre-trained CNNs to extract hierarchical image features from the skin lesion images.
3) The system as claimed in claim 1, wherein the feature fusion module further includes:
- a K-means-based clustering component that groups similar features into clusters and generates a K-means feature set (KFS);
- a DBSCAN-based clustering component that groups features based on density regions to create a DBSCAN feature set (DFS); wherein both the KFS and DFS represent fused feature sets derived from clustering-based feature fusion.

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