An Integrated Deep Learning Approach for PCOS Diagnosis using Ultrasound images and Clinical parameters

Authors: Thenmozhi, Ramya R, Suganya S, Susithra R

Abstract: Polycystic Ovary Syndrome (PCOS) is a common hormonal disorder among women of reproductive age, characterized by complex clinical symptoms and varying ultrasound findings, which often make diagnosis challenging. To address these limitations, this study proposes an integrated deep learning–based diagnostic framework that combines ovarian ultrasound images with key clinical parameters. Convolutional Neural Networks (CNNs) are utilized to automatically learn representative features from ultrasound images, enabling the identification of ovarian morphological patterns such as follicular distribution and ovarian size. Simultaneously, relevant clinical data including age, body mass index, hormonal levels, menstrual history, and metabolic indicators are analyzed using a neural network model. The learned features from both modalities are fused to improve diagnostic performance. The proposed approach minimizes reliance on subjective clinical assessment and manual feature extraction. Experimental evaluation demonstrates that the integrated model achieves superior accuracy, sensitivity, and specificity compared to single-modal diagnostic methods. This framework provides an effective and non-invasive decision-support tool for early and reliable PCOS diagnosis.

DOI: https://doi.org/10.5281/zenodo.18755808