Improved Identification of Polycystic Ovary Syndrome by Ultrasound Imaging Employing Multi-Stage Image Processing and Deep Learning-Driven Segmentation with Hybrid Classification Methods

Abstract

Polycystic Ovary Syndrome (PCOS) is a widespread hormone illness impacting women of adulthood, usually defined by the emergence of many follicles within the ovaries and hormone dysregulation. Timely and precise identification by ultrasound imaging is essential to successful assessment & therapy. This research introduces a computerized method for the diagnosis & categorization of PCOS utilizing images and ML methodologies. The proposed framework starts with the preparation of ovary ultrasound images utilizing noise removal techniques, including Speckle Reducing Anisotropic Diffusion (SRAD), and contrast enhancement by Contrast Limited Adaptive Histogram Equalization (CLAHE). The improved pictures undergo U-Net segmentation technique to figure out the area of interest (ROI) and detect follicular features. Following that, mobileNetV2 technique is used for feature extraction.  MobileNetV2 recovers organizational characteristics at many levels, encompassing low-level boundary details, mid-level texture patterns, and high-level semantic illustrations of ovary architecture.  Classification is ultimately performed using machine learning models, such Support Vector Machine (SVM) and Convolutional Neural Network (CNN) to differentiate between PCOS and normal instances. Experimental findings indicate that the suggested design enhances diagnostic precision.