Enhancing the Detection of PCOS using Deep Learning

Polycystic Ovary Syndrome (PCOS) is a widespread endocrine disorder affecting women of reproductive age, frequently resulting in infertility due to hormonal abnormalities. Symptoms include severe acne, irregular menstrual cycles, excess body hair, and weight gain. Early and accurate diagnosis of PCOS is critical for effective management and minimizing related health risks. Traditional manual identification of ovarian follicles in ultrasound images by radiologists can lead to misdiagnoses. To overcome this, proposed an innovative PCOS classification approach that integrates the encoder part of a Transformer model with various pretrained CNN models for robust feature extraction from ultrasound images. This hybrid model leverages advanced deep learning and attention mechanisms to significantly improve diagnostic precision. Our study aims to deliver a highly accurate and comprehensive PCOS detection method, enabling early intervention and customized treatment plans. This cutting-edge approach, situated at the intersection of artificial intelligence and women’s health, holds the potential to transform PCOS management and enhance healthcare outcomes, representing a notable advancement in the field.