Enhanced Endometriosis Detection Using the Deep Feature Enquiring Based on Hyper Capsule Resnet50-CNN Algorithm

Abstract

Endometriosis is a chronic condition in which the lining of the uterus grows outside the uterus, causing pain, swelling, and fertility problems. It usually affects the ovaries, fallopian tubes, and pelvic lining, leading to severe menstrual cramps and other complications. Traditional methods for diagnosing endometriosis, such as laparoscopy and ultrasound, are often invasive, time-consuming, and can lead to delayed diagnosis. Relying on symptom-based assessment lacks accuracy, making early and affective treatment challenging. To solve the problem a novel Hyper capsule Resnet50-CNN algorithm is introduced for classifying the ovarian cysts by utilizing the ultrasound images processed datasets and applied to the image processing technique. Initially, Butterworth Filter preprocessing enhanced the details of the input data set and gave a clear view of the input dataset. Modified Watershed Segmentation algorithm (MWSA) separates follicles or cysts that specifically differentiate for selection features. An improved Recursive Bee Colony (RBC) Feature Selection algorithm is trained to identify biologically significant markers, ensuring accurate feature extraction without errors. ResNet50 with CNN architecture is a deep learning approach to extract complex features methodically hyper capsule ResNet50 contains 50 layers of network operation, which is a disappearing gradient issue that is frequently observed. RESNET 50 classification identifies ovarian cysts into three stages based on the condition: regular nodule, ovarian growth, and polycystic ovary. The accuracy is 94.15%, sensitivity 95.82%, Specificity 94.54%, FI– Score 94.89% and RMSE 84.25% measure parameters are analyzed, and the performance Matrix obtains results.