Smart Diagnosis and Early Intervention in PCOS: A Deep Learning Approach to Women's Reproductive Health

TL;DR

This paper presents a deep learning framework using transfer learning with DenseNet201 and ResNet50 for accurate classification of ovarian ultrasound images to facilitate early PCOS diagnosis and intervention.

Contribution

It introduces a novel application of transfer learning with data augmentation and explainability techniques for PCOS diagnosis from ultrasound images.

Findings

Achieved 99.80% validation accuracy with DenseNet201.

Used explainable AI methods to enhance model transparency.

Demonstrated potential for clinical application in women's reproductive health.

Abstract

Polycystic Ovary Syndrome (PCOS) is a widespread disorder in women of reproductive age, characterized by a hormonal imbalance, irregular periods, and multiple ovarian cysts. Infertility, metabolic syndrome, and cardiovascular risks are long-term complications that make early detection essential. In this paper, we design a powerful framework based on transfer learning utilizing DenseNet201 and ResNet50 for classifying ovarian ultrasound images. The model was trained on an online dataset containing 3856 ultrasound images of cyst-infected and non-infected patients. Each ultrasound frame was resized to 224x224 pixels and encoded with precise pathological indicators. The MixUp and CutMix augmentation strategies were used to improve generalization, yielding a peak validation accuracy of 99.80% by Densenet201 and a validation loss of 0.617 with alpha values of 0.25 and 0.4, respectively. We evaluated the model's interpretability using leading Explainable AI (XAI) approaches such as SHAP, Grad-CAM, and LIME, reasoning with and presenting explicit visual reasons for the model's behaviors, therefore increasing the model's transparency. This study proposes an automated system for medical picture diagnosis that may be used effectively and confidently in clinical practice.