Unmasking Poly Cystic Ovarian Syndrome(PCOS): Harnessing Deep Learning in Ultrasound Imaging Analysis

A large proportion of women globally suffer from PCOS, a hormonal condition that impacts reproductive health and poses major dangers to their metabolic and cardiovascular health. PCOS diagnosis at an early stage is crucial to mitigate these risks and provide timely interventions. The challenge in diagnosing PCOS is to count the follicles and calculate their volume in the ovaries, which is currently done manually by doctors and radiologists utilising ovary ultrasonography. In this study, a shallow robust deep learning model is proposed with three alternate convolution and max pooling layers followed by flatten, dropout and dense layer that automatically detects PCOS from ultrasound images with low computational complexity. The performance of the proposed model is compared with the Inception V3 and Dense Net 201 deep learning models. The benchmark PCOS dataset from Kaggle was used for the study and dataset was split as 70:30 for training and testing. In conclusion, our study highlights the potential of deep learning in the field of gynaecology and reproductive medicine. It can revolutionise PCOS diagnosis and contribute to better health outcomes for women with PCOS.