A COMPREHENSIVE REVIEW OF CLASSIFICATION TECHNIQUES FOR ENDOMETRIOSIS DISEASE IDENTIFICATION

Medical disorders in women can often be the underlying cause of various symptoms and are frequently associated with anovulatory conditions, such as Endometriosis. The limitation of finding the specific diseases in image processing approach is complex structure tissue, early detection and treatment of these conditions are essential. To address these challenges, this review research with Multiple Machine Learning (ML) approaches such as Gradient Boosted Decision Tree, SE-ResNet-34 network, and CNNbased deep learning, for classification purpose for diseases identification. The datasets taken an ultrasound image related to Endometriosis, obtained from open-source platforms provided by women's healthcare facilities. Prior to analysis, these input images undergo data preprocessing techniques to enhance their quality and relevance, facilitating accurate evaluation of Endometriosis cases. The CNN network architecture is applied to extract intricate features from the input datasets. SE-ResNet-34 networks are particularly effective for image classification tasks due to their ability to address the advanced gradient problem, allowing for the construction of deeper network architectures with enhanced performance. Similarly, CNN, a powerful ensemble learning method, improves predictive accuracy by iteratively reducing classification errors. Performance metrics such as accuracy, sensitivity, and F1-score are used to evaluate the efficacy of these algorithms in the early diagnosis of Endometriosis and improving healthcare outcomes for women.