Diagnosis of Polycystic Ovarian Syndrome with the Implementation of Deep Learning Models

A prevalent endocrine disorder affecting reproductive-age women is polycystic ovarian syndrome (PCOS). Symptoms include infertility, abnormal menstrual cycles, and excessive hair growth are caused by hormonal imbalances. Identification at an early stage is absolutely necessary for efficient management and avoidance of the related health hazards. In this article, we offer a comprehensive method for the diagnosis of polycystic ovary syndrome (PCOS). This method involves the utilization of pre-processing and segmentation techniques, followed by classification based on Convolutional Neural Networks (CNN). Flipping, rotating, zooming, and rescaling are some of the image enhancement techniques that are utilized during the preprocessing step. These techniques help to standardize the inputs that are for analysis. Segmentation techniques are utilized to efficiently designate structures by isolating regions of interest. These approaches include size-based filtering, contour detection, density analysis, color-based segmentation, edge detection, texture analysis, location-based segmentation, and watershed transformation. An essential component of our methodology is the extraction and classification of features using CNN, which allows us to achieve a high level of accuracy in discriminating between PCOS-positive and PCOS-negative instances. By integrating a variety of approaches, effective pre-processing and segmentation can be achieved. Convolutional neural networks (CNNs) automate feature extraction, which improves model generalization. Our method offers a potential strategy for reliable PCOS detection from medical images, which will assist physicians in early diagnosis and therapy, ultimately leading to improvements in patient outcomes and the quality of care provided. It compared CNN to Resnet and GNB, among others, and found that CNN consistently outperformed the competition in all four metrics we measured: accuracy, recall, F1 score, and precision. According to the findings of our investigation, CNN consistently outperforms other models in terms of accuracy, recall, F1 score, and precision. As a result, it is the most trustworthy model, with an accuracy of roughly 0.98. The accuracy of 97% is achieved.