Prompt diagnosis of polycystic ovary syndrome using ultrasonography – A machine learning approach

• Main Authors: Reka, S., Mohan, Karthik, Praba, T. Suriya
• Format: Conference Proceeding
• Language: English
• Subjects: Algorithms; Datasets; Deep learning; Diagnosis; Generative adversarial networks; Machine learning; Medical imaging; Ovaries; Polycystic ovary syndrome; Ultrasonic imaging
• ISSN: 0094-243X; 1551-7616
• DOI: 10.1063/5.0194703

Polycystic Ovary Syndrome (PCOS) is a common hormonal disorder among women of reproductive age and is characterised by disrupted ovulation cycles and an increase in androgen secretion. It is allied with metabolic and cardiovascular risks as well as a greater risk of developing hormone dependent-cancer. Due to its high prevalence in the current society coupled with its potential hazards, its early and accurate diagnosis is of utmost importance. The diversity of symptoms often delays the process of confirmatory diagnosis and more than 50% of women remain undiagnosed. To address these issues by facilitating rapid detection, this article proposes early diagnosis of PCOS with hormonal test and ultrasound images using various Machine Learning and Deep Learning algorithms. In this paper, we have used Generative Adversarial Networks to extend our dataset, and Transfer Learning using Residual Networks (ResNets) and Inception Networks for the ultrasound images. For the hormonal dataset, after pre-processing, we have compared the performance of multiple models, where CatBoost produces the highest accuracy.