AN AI BASED METHOD FOR EARLY DETECTION OF CERVICAL CANCER

Abstract

The present invention discloses an AI-based method for the early detection of cervical cancer using Pap smear images. The system employs an enhanced fully connected neural network (FCNN) to analyze cervical cell features and identify abnormalities with high precision. The proposed framework begins with the acquisition of Pap smear images, which undergo preprocessing steps including normalization, augmentation, and segmentation to enhance image quality and isolate regions of interest. The enhanced FCNN architecture incorporates multiple densely connected layers optimized for extracting discriminative features from high-dimensional image data. Advanced regularization techniques such as dropout and batch normalization are integrated to prevent overfitting and stabilize learning. The network utilizes skip connections to retain low-level features critical for detecting early-stage abnormalities. Clinical metadata, such as patient history and test results, is optionally incorporated into the system, merging with extracted features to improve diagnostic accuracy. The final feature vector is passed through a classification layer employing a softmax function to predict the presence of pre-cancerous or cancerous conditions. The system achieves robust performance through fine-tuning hyperparameters and explainable AI methods, providing visual insights into detected anomalies for enhanced interpretability. This invention offers a non-invasive, automated, and highly accurate solution for early cervical cancer detection, enabling timely diagnosis and treatment while reducing reliance on subjective manual evaluation methods. The invention benefits many stakeholders, including healthcare departments, governments, healthcare industry, healthcare professionals, researchers, and academic institutions.

Specification

Description:FIELD OF INVENTION
This invention presents an AI-based method for the early detection of cervical cancer using Pap smear images. It utilizes an enhanced fully connected neural network (FCNN) to analyze cervical cell features and accurately identify abnormalities. The FCNN architecture incorporates densely connected layers optimized for extracting relevant features and includes regularization techniques, such as dropout and batch normalization, to prevent overfitting. Skip connections retain essential low-level features critical for detecting early-stage abnormalities. The final feature vector is processed through a classification layer using a softmax function to predict pre-cancerous or cancerous conditions. This method utilizes Pap smear images as input. These images are processed using an AI-based algorithm, which is designed to identify early signs of cervical cancer. The algorithm analyzes various features of the cells in the Pap smear image, such as their size, shape, and texture, to determine the likelihood of malignancy. Once the algorithm has processed the image, it generates results and performance statistics. These statistics may include metrics such as sensitivity, specificity, and accuracy, which assess the effectiveness of the AI-based method in detecting cervical cancer. The flowchart highlights the key steps involved in this AI-based approach, from the input of Pap smear images to the generation of results and performance statistics.

Figure 2 illustrates a flowchart outlining a simplified functional flow of the AI-based method for early detection of cervical cancer. The process begins with cervical images, specifically Pap smear images. These images are then subjected to pre-processing and feature selection. This step involves cleaning and enhancing the images to remove noise and artifacts, as well as identifying the most relevant features that are indicative of cancerous cells. The enhanced images with selected features are then fed into a fully connected neural network. This neural network is trained on a large dataset of labeled Pap smear images to learn to recognize patterns associated with cancerous cells. Once the neural network has been trained, it can be used to analyze new Pap smear images and generate early cervical cancer results. These results may include a classification of the image as normal or abnormal, as well as additional information about the severity and type of any detected abnormalities. Finally, the system generates performance statistics to evaluate the accuracy and effectiveness of the AI-based method. These statistics may include metrics such as sensitivity, specificity, and accuracy, which assess the ability of the method to correctly identify both cancerous and non-cancerous cases. This simplified flowchart provides a high-level overview of the key steps involved in the AI-based method for early detection of cervical cancer. It highlights the essential components, from image pre-processing and feature selection to neural network analysis and result generation.

BACKGROUND OF THE INVENTION
According to the World Health Organization, various types of cancer are causing significant health issues and deaths worldwide. Cervical cancer is one such type that occurs frequently and can lead to severe consequences, including death. With the rise of artificial intelligence, it is crucial to utilize technology-driven approaches to enhance the early diagnosis of cervical cancer. Doing so will assist healthcare professionals in developing better treatment plans, improving patient outcomes, and fostering greater patient engagement. The following are the relevant patents.

US 7.664,300 B2: Uterine cervical cancer Computer-Aided-Diagnosis (CAD) according to this invention consists of a core processing system that automatically analyses data acquired from the uterine cervix and provides tissue and patient diagnosis, as well as adequacy of the examination. The data can include, but is not limited to, color still images or video, reflectance and fluorescence multi-spectral or hyper-spectral imagery, coherent optical tomography imagery, and impedance measurements, taken with and without the use of contrast agents like 3-5% acetic acid, Lugol's iodine, or 5-aminolevulinic acid.

US 11,508,168 B2: Systems , methods , devices , and other techniques using machine learning for interpreting , or assisting in the inter pretation of , biologic specimens based on digital images are provided . Methods for improving image - based cellular identification , diagnostic methods , methods for evaluating effectiveness of a disease intervention , and visual outputs useful in assisting professionals in the interpretation of biologic specimens are also provided .

US11315688B2: A system for an artificial intelligence alimentary professional support network for vibrant constitutional guidance includes a computing device. The system includes a diagnostic engine designed and configured to receive a biological extraction from a user and generate a diagnostic output based on the biological extraction. The system includes an advisor module designed and configured to receive a request for an advisory input, generate an advisory output using the request for an advisory input and the diagnostic output, and transmit the advisory output. The system includes an alimentary input module designed and configured to receive the advisory output, select an informed advisor alimentary professional client device as a function of the request for an advisory input, and transmit the at least an advisory output to the informed advisor alimentary professional client device.

US20200394565A1: An artificial intelligence behavior modification support system includes a diagnostic engine operating on the at least a server and configured to receive at least a biological extraction from a user and generate at least a request for a behavior modification. The system includes an influencer module designed and configured to generate at least a request for an influencer as a function of the at least a request for a behavior modification. The system includes a client interface module designed and configured to transmit the at least a request for an influencer to at least a client device.the at least an advisory output to the informed advisor alimentary professional client device.

US20200005901A1: Disclosed herein are classifier models, computer implemented systems, machine learning systems and methods thereof for classifying asymptomatic patients into a risk category for having or developing cancer and/or classifying a patient with an increased risk of having or developing cancer into an organ system-based malignancy class membership and/or into a specific cancer class membership.
The above patents lack the comprehensive approach and adaptive nature for AI-driven early detection of cervical cancer.

OBJECTS OF THE INVENTION
1] Therefore, the object of the present invention is to have a system and method for AI-driven early diagnosis of cervical cancer.

2] Another object of the present invention is a module for feature engineering for leveraging the training process.

3] Another object of the present invention is an enhanced, fully connected neural network for early detection of cervical cancer.

4] Another object of the present invention is an adaptive training phase for improving the model with incremental knowledge.

5] Another object of the present invention is a learning-based cervical cancer detection method.

6] Yet another important object of the present invention is a solution to the problem of early detection of cervical cancer using AI-enabled methods.

STATEMENT OF THE INVENTION
The present invention, known as "An AI Based Method for Early Detection of Cervical Cancer," is meant for an AI-based method for the early detection of cervical cancer using Pap smear images. It utilizes an enhanced fully connected neural network (FCNN) to analyze cervical cell features and accurately identify abnormalities. The FCNN architecture incorporates densely connected layers optimized for extracting relevant features and includes regularization techniques, such as dropout and batch normalization, to prevent overfitting. Skip connections are used to retain essential low-level features critical for detecting early-stage abnormalities. The final feature vector is processed through a classification layer using a softmax function to predict pre-cancerous or cancerous conditions. The process begins with cervical images, specifically Pap smear images. These images are then subjected to pre-processing and feature selection. This step involves cleaning and enhancing the images to remove noise and artifacts, as well as identifying the most relevant features that are indicative of cancerous cells. The enhanced images with selected features are then fed into a fully connected neural network. This neural network is trained on a large dataset of labeled Pap smear images to learn to recognize patterns associated with cancerous cells. Once the neural network has been trained, it can be used to analyze new Pap smear images and generate early cervical cancer results. These results may include a classification of the image as normal or abnormal, as well as additional information about the severity and type of any detected abnormalities. Finally, the system generates performance statistics to evaluate the accuracy and effectiveness of the AI-based method. These statistics may include metrics such as sensitivity, specificity, and accuracy, which assess the ability of the method to correctly identify both cancerous and non-cancerous cases. This simplified flowchart provides a high-level overview of the key steps involved in the AI-based method for early detection of cervical cancer. It highlights the essential components, from image pre-processing and feature selection to neural network analysis and result generation.

BRIEF DESCRIPTION OF THE DRAWING
This invention presents an AI-based method for the early detection of cervical cancer using Pap smear images. It utilizes an enhanced fully connected neural network (FCNN) to analyze cervical cell features and accurately identify abnormalities. The FCNN architecture incorporates densely connected layers optimized for extracting relevant features and includes regularization techniques, such as dropout and batch normalization, to prevent overfitting. Skip connections are used to retain essential low-level features critical for detecting early-stage abnormalities. The final feature vector is processed through a classification layer using a softmax function to predict pre-cancerous or cancerous conditions. The current invention is illustrated with the many drawings given below.




Figure 1: Overview of the Current Invention titled "An AI-Based Method for Early Detection of Cervical Cancer"




Figure 2: Short Functional Flow of the Current Invention titled "An AI-Based Method for Early Detection of Cervical Cancer"









Figure 3: Detailed Functional Flow of the Current Invention titled "An AI-Based Method for Early Detection of Cervical Cancer"











Figure 4: Architecture of Enhanced Fully Connected Neural Network (FNN) Involved in the Current Invention titled "An AI-Based Method for Early Detection of Cervical Cancer


Figure 5: Illustrates stakeholders for which the invention is beneficial

DETAILED DESCRIPTION OF DRAWINGS
This invention presents an AI-based method for the early detection of cervical cancer using Pap smear images. It utilizes an enhanced fully connected neural network (FCNN) to analyze cervical cell features and accurately identify abnormalities. The FCNN architecture incorporates densely connected layers optimized for extracting relevant features and includes regularization techniques, such as dropout and batch normalization, to prevent overfitting. Skip connections are used to retain essential low-level features critical for detecting early-stage abnormalities. The final feature vector is processed through a classification layer using a softmax function to predict pre-cancerous or cancerous conditions. The details of the drawings are provided in the preceding section.

Figure 1 illustrates a flowchart outlining an AI-based method for early detection of cervical cancer. This method utilizes Pap smear images as input. These images are processed using an AI-based algorithm, which is designed to identify early signs of cervical cancer. The algorithm analyzes various features of the cells in the Pap smear image, such as their size, shape, and texture, to determine the likelihood of malignancy. Once the algorithm has processed the image, it generates results and performance statistics. These statistics may include metrics such as sensitivity, specificity, and accuracy, which assess the effectiveness of the AI-based method in detecting cervical cancer. The flowchart highlights the key steps involved in this AI-based approach, from the input of Pap smear images to the generation of results and performance statistics.

Figure 2 illustrates a flowchart outlining a simplified functional flow of the AI-based method for early detection of cervical cancer. The process begins with cervical images, specifically Pap smear images. These images are then subjected to pre-processing and feature selection. This step involves cleaning and enhancing the images to remove noise and artifacts, as well as identifying the most relevant features that are indicative of cancerous cells. The enhanced images with selected features are then fed into a fully connected neural network. This neural network is trained on a large dataset of labeled Pap smear images to learn to recognize patterns associated with cancerous cells. Once the neural network has been trained, it can be used to analyze new Pap smear images and generate early cervical cancer results. These results may include a classification of the image as normal or abnormal, as well as additional information about the severity and type of any detected abnormalities. Finally, the system generates performance statistics to evaluate the accuracy and effectiveness of the AI-based method. These statistics may include metrics such as sensitivity, specificity, and accuracy, which assess the ability of the method to correctly identify both cancerous and non-cancerous cases. This simplified flowchart provides a high-level overview of the key steps involved in the AI-based method for early detection of cervical cancer. It highlights the essential components, from image pre-processing and feature selection to neural network analysis and result generation.

Figure 3 illustrates a detailed flowchart outlining the functional flow of the AI-based method for early detection of cervical cancer. The process begins with cervical images, specifically Pap smear images. These images undergo data pre-processing, which involves normalization and augmentation. Normalization ensures that the images are scaled to a consistent range, while augmentation artificially creates variations in the images to improve the model's ability to generalize. Next, feature engineering is performed using two separate feedforward neural networks (EFNNs). The first EFNN extracts relevant features from the pre-processed images, while the second EFNN selects the most informative features for further analysis. The selected features are then used to train the main model, which is also an EFNN. The hyperparameters of this model are optimized using enhanced Bayesian optimization, a technique that efficiently explores the parameter space to find the best configuration. Once the model is trained, it can be used to detect cervical cancer in new Pap smear images. If the model detects a potential malignancy, an alert is generated to notify the healthcare provider. Additionally, performance evaluation is conducted to assess the accuracy and effectiveness of the model using various metrics. This detailed architecture provides a comprehensive overview of the various components and steps involved in the AI-based method for early detection of cervical cancer. It emphasizes the importance of data pre-processing, feature engineering, model training, and performance evaluation in ensuring the accuracy and reliability of the system.

Figure 4 illustrates the architecture of an Enhanced Fully Connected Neural Network (FNN) used in the AI-based method for early detection of cervical cancer. This FNN consists of multiple layers, including input, hidden, and output layers. The input layer receives the pre-processed and feature-engineered Pap smear images. These images are then passed through a series of hidden layers, each composed of multiple neurons. The neurons in each layer are fully connected to the neurons in the previous layer, allowing for complex feature extraction and learning. The hidden layers in this FNN employ a combination of techniques to improve its performance. Dense layers use the ReLU activation function, which introduces non-linearity to the network, enabling it to learn complex patterns. Dropout is a regularization technique that randomly drops out neurons during training, preventing overfitting and improving generalization. Batch normalization helps stabilize training by normalizing the input to each layer, leading to faster convergence and better performance. The final output layer consists of a single neuron with a softmax activation function. This layer generates a probability distribution over the possible classes (e.g., normal, benign, malignant), representing the likelihood of the input image belonging to each class. This FNN architecture, with its combination of dense layers, dropout, and batch normalization, enables the model to effectively learn complex patterns from the Pap smear images and accurately classify them as normal or cancerous.

Figure 5 illustrates that the current invention benefits many stakeholders. These include healthcare departments, governments, the healthcare industry, healthcare professionals, researchers, and academic institutions.
, Claims:I Claim
1. A system and method for AI-driven early diagnosis of cervical cancer.

2. A module for feature engineering for leveraging the training process.

3. An enhanced fully connected neural network for early detection of cervical cancer.

4. An adaptive training phase for improving the model with incremental knowledge.

5. A learning-based cervical cancer detection method.

6. A solution to the problem of early detection of cervical cancer using AI-enabled method.

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