Detection and Prediction of Polycystic Ovary Syndrome Using Attention-Based CNN-RNN Classification Model

Abstract

Polycystic Ovary Syndrome (PCOS) has many challenges when it comes to its diagnosis and treatment due to the diversity of presentation and potential long-term consequences for health. For this reason, sophisticated data pre-processing and classification methods are implemented to enhance the accuracy of PCOS diagnosis. A number of innovative techniques are employed in the process to enhance the accuracy and reliability of PCOS diagnosis. To identify ovarian cysts, real-time ultrasound images are pre-processed initially with the Contrast-Limited Adaptive Histogram Equalization (CLAHE) model to improve image contrast and sharpness. The ultrasound images are segmented with the K-means clustering algorithm, Particle Swarm Optimization (PSO), and a fuzzy filter, enabling precise analysis of regions of interest. An attention-based Convolutional Neural Network-Recurrent Neural Network (CNN-RNN) model is employed for classification and does so effectively to capture the temporal and spatial characteristics of the segmented data. The proposed model has a very good accuracy rate of 96% and works very well on a variety of evaluation metrics such as accuracy, precision, sensitivity, F1-score, and specificity. The results are evidence of the robustness of the model in minimizing false positives and enhancing PCOS diagnostic accuracy. Nevertheless, it is noted that bigger data sets are required to maximize the precision and generalizability of the model. The aim of subsequent research is to use Explainable AI (XAI) methods to enhance clinical decision-making and establish trust by making the model's predictions clearer and understandable for patients and clinicians. Along with enhancing PCOS detection, this comprehensive approach sets a precedent for integrating explainability into AI-based medical diagnostic devices.