Hemocare360: Integrated Intracranial Hemorrhage Detection and Patient Management System

Abstract

Intracranial hemorrhage (ICH) is a severe medical condition necessitating immediate diagnosis for improved patient outcomes. This study presents Hemocare360, an advanced system designed for integrated ICH detection and patient management. Hemocare360 employs a graph cut algorithm for precise brain segmentation and uses deep learning models for robust feature extraction. The system combines these segmentation masks with XGBoost classification to accurately distinguish between hemorrhagic and nonhemorrhagic regions. Furthermore, Hemocare360 offers a comprehensive user interface that supports easy image upload, detailed result interpretation, and patient data management, facilitating rapid clinical decision-making. Our results demonstrate that Hemocare360 not only achieves high accuracy in ICH detection but also enhances patient management, thereby significantly improving patient care in critical situations.

Specification

Description:FIELD OF INVENTION

In the field of medical imaging, intracranial hemorrhage (ICH) is a life-threatening condition requiring immediate and precise diagnosis. ICH manifests as bleeding within or around brain tissue, commonly resulting from trauma or vascular abnormalities. The accurate classification of ICH subtypes such as intraparenchymal, intraventricular, subarachnoid, subdural and epidural hemorrhages is crucial for effective treatment. Traditional methods rely on CT scans, analyzed by skilled radiologists. However, this process can be time-consuming due to the complexity of brain images and the limited availability of specialists, particularly in emergency settings. To address this challenge, artificial intelligence (AI) has been integrated into healthcare, but many AI-based systems struggle with accuracy in detecting subtle hemorrhages. Hemocare360 introduces an advanced, automated solution that enhances the precision and speed of ICH detection using a combination of deep learning, XGBoost and a graph cut algorithm for segmentation. Pre-trained models extract robust features and a soft voting mechanism integrates these features for a comprehensive analysis. Additionally, the system is designed with a user-friendly interface and an AI-powered chatbot for seamless interaction. Hemocare360 optimizes diagnostic workflows and enhances patient care by providing accurate, real-time assessments in critical scenarios, thus offering a cutting-edge tool to combat the global burden of ICH.

BACKGROUND OF THE INVENTION

Intracranial hemorrhage (ICH) detection is a critical area of medical research, where various machine learning and deep learning techniques have been developed to improve accuracy and efficiency in diagnosing brain hemorrhages. One prominent approach employs machine learning algorithms like Support Vector Machines (SVM), Random Forest (RF) and Decision Trees. These models typically follow a structured process involving steps such as bone removal, feature extraction, feature selection using Principal Component Analysis (PCA) and final classification. Among these, Random Forest models have demonstrated particularly high accuracy, with some studies reporting an accuracy rate of over 90%.Another important development involves unsupervised systems for ICH segmentation. Techniques such as mixture models combined with the Expectation-Maximization algorithm allow for the segmentation of hemorrhagic tissue from healthy brain matter without the need for extensive labeled training data. This is particularly useful in scenarios with limited access to annotated datasets. These models leverage the distinct statistical distributions of hemorrhage and healthy tissues, leading to strong performance even with limited supervision. Artificial Neural Networks (ANNs) have also been explored for brain hemorrhage diagnosis, typically in conjunction with image segmentation techniques like the watershed method. These models are designed to overcome the limitations of traditional methods, offering improvements in both accuracy and usability. Systems that have been evaluated by medical professionals, such as radiologists, have shown promising results, indicating that ANN-based approaches can be practical in clinical settings.In recent years, deep learning has revolutionized ICH detection, with architectures like ResNet, DenseNet and Inception-V4 playing a significant role in both detection and classification. These models have been particularly effective in addressing the complexity of multi-class ICH classification by leveraging pre-trained weights and fine-tuning with domain-specific data. Techniques such as transfer learning and optimization methods like Bayesian Optimization have further enhanced the performance and reliability of these systems.

SUMMARY OF THE INVENTION
In this study, we propose a comprehensive pipeline for image analysis and classification, as depicted in Figure 1.1. The pipeline integrates advanced techniques in computer vision and machine learning to enhance the quality and accuracy of image analysis. The process begins with data preprocessing to improve image quality, followed by image segmentation utilizing a graph cut algorithm to partition images effectively. For feature extraction, three pre-trained models MobileNet-V2, EfficientNet-B0 and DenseNet-121 are employed.



Fig 1.1 Architecture of the Invention
These models extract features from segmented images, which are then combined using a soft voting approach to generate a robust feature representation. Location detection is achieved through segmentation masks, which facilitate precise identification of regions of interest within the images. Classification is performed using an XGBoost model trained on the concatenated features, ensuring accurate categorization of the detected regions. Additionally, the system is integrated with an AI chatbot to enhance user interaction and assistance. Comprehensive reports can be generated and downloaded for further analysis, demonstrating a holistic approach to image analysis leveraging cutting-edge technologies.
I.Image Segmentation: The Graph Cut algorithm is an advanced image segmentation technique that partitions an image into distinct regions by modeling it as a graph. This section provides a detailed explanation of the methodology used for applying the Graph Cut algorithm to generate segmented masks from preprocessed images, including the relevant mathematical functions. Before applying the Graph Cut algorithm, the image data must be preprocessed to ensure it is suitable for segmentation. This includes normalization and resizing to standardize the input images, ensuring consistency and comparability for effective segmentation.
II.Performance metrics for DenseNet-121: DenseNet-121 has demonstrated outstanding performance in image classification tasks. The model shows high precision, recall, and F1-score, indicating its ability to effectively identify true positives while minimizing false positives and negatives. The overall accuracy of DenseNet-121 reflects its proficiency in capturing complex patterns within medical images and provides a robust solution for various classification challenges as shown in TABLE 1.1, the performance metrics for various models are detailed, indicating the superiority of DenseNet-121 in terms of recall, accuracy, precision, and F1-score.

Model Recall Accuracy Precision F1- Score
Inception-V4 0.69 0.70 0.79 0.76
SLEX-Net 0.72 0.77 0.77 0.77
ResNet-101 0.76 0.79 0.80 0.80
DenseNet-121 0.82 0.85 0.89 0.85
Table 1.1 Performance Metric of DenseNet-121 Vs Other Models
III.Performance Metrics for EfficientNet-B0: The classification report for EfficientNet-B0 depicted in Table 1.2 summarizes precision, recall, and F1-score metrics for individual classes. These metrics offer a comprehensive view of the model's performance, helping to assess trade-offs and guiding optimization strategies. Regular examination of these metrics informs adjustments to enhance EfficientNet-B0's performance across diverse classification scenarios.

Model Recall Accuracy Precision F1- Score
RNN 0.83 0.88 0.88 0.86
DenseNet 0.82 0.85 0.89 0.80
EfficientNet-B0 0.89 0.90 0.91 0.89

Table 1.2 Performance Metric of EfficientNet-B0
IV.Performance Metrics for MobileNet-V2: MobileNet-V2's classification report, illustrated in Table 1.3 details precision, recall, and F1-score metrics. Precision measures the accuracy of positive predictions, while recall assesses the model's ability to identify all relevant instance. The F1-score, as the harmonic mean of precision and recall, offers a balanced evaluation of the model's performance.

Model Recall Accuracy Precision F1- Score
CNN 0.89 0.88 0.91 0.88
ResNet 0.89 0.90 0.90 0.86
MobileNet-V2 0.94 0.95 0.94 0.90
Table 1.3 Performance Metric of MobileNet-V2
V.Soft Voting: Soft voting, depicted in Figures 1.2, combines predictions from multiple models by averaging their probability distributions. Each model's output probability distribution is considered, and the final prediction is made by averaging these probabilities. This approach allows for more nuanced decision-making and can improve overall prediction accuracy, particularly when individual models excel in different areas or exhibit varying degrees of confidence. Soft voting is commonly used in classification and regression tasks to leverage the combined expertise of multiple models for more robust and reliable predictions.
Fig 1.2 Weighted Value for Soft Voting
VI.Location Detection: Figure1.3 illustrates location detection using contour detection techniques. Contour detection involves identifying and tracing the boundaries of objects or shapes within an image. By analyzing contours, such as those of buildings or landmarks, and their relative positions, location information can be inferred. This technique enables the extraction of spatial features, facilitating tasks such as object localization and scene understanding. Contour detection algorithms, such as Canny edge detection and contour-based segmentation, play a crucial role in various applications, including object detection, image retrieval, and augmented reality.

Fig 1.3 Location Detection Using Segmentation Mask
VII.Classification using XGBoost: XGBoost an ensemble learning method that combines weak learners like decision trees, is used for classification tasks. It assigns weights to instances and adjusts them based on the performance of individual weak learners. Known for handling imbalanced datasets and boosting overall performance, XGBoost is a popular choice for classification challenges shown in table 1.4.




Ref. Model Accuracy
Asif Muhammad [6] ResNet-121+Inception-V3 0.93
Majeed [14] SVM+RF+PCA 0.88
Rajagopal [19] Conv-LSTM 0.95
Proposed DenseNet+MobileNet+MobileNet
+ XGBoost 0.96
Table 1.4 Comparison of Classification using XGBoost , Claims:1.The system utilizes advanced imaging and monitoring technologies to identify intracranial hemorrhages quickly, enabling timely intervention and potentially reducing mortality.
2.It is most light weighted design. So the user has very good comfort to carry.
3.Apart from existing models, this model also mentions the name of the obstacle.
4.The system aggregates patient data for comprehensive analysis, providing healthcare professionals with valuable insights into patient trends, treatment efficacy and potential complications which can improve decision-making.

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