THE PATHOGENESIS OF POLYCYSTIC OVARY SYNDROME (PCOS): THE HYPOTHESIS OF FUNCTIONAL OVARIAN HYPERANDROGENISM REVISITED

Abstract

Polycystic ovary syndrome (PCOS) is one of the most common endocrine illnesses, affecting millions of women worldwide and having a substantial influence on their reproductive, metabolic, and psychological health. It is distinguished by hormonal imbalances, irregular menstrual cycles, and the presence of cysts in the ovaries, which frequently lead to infertility and increased risk of comorbidities such as diabetes, obesity, and cardiovascular disease. Early identification of PCOS is crucial for reducing these health risks, since prompt interventions and specialised therapies can considerably improve patient outcomes. Traditional diagnostic approaches, such as physical exams, ultrasound imaging, and hormonal assessments, are frequently time-consuming, subjective, and prone to discrepancies owing to differences in competence and equipment quality. This paper presents a unique technique to PCOS identification based on an upgraded VGG16 deep learning model designed exclusively for medical imaging data. The study's dataset consists of 11,784 photos, 6,784 of which are infected and 5,000 of which are uninfected, rigorously classified into two categories. The dataset was divided into training and testing subsets at an 80:20 ratio to guarantee complete examination, yielding 9,428 pictures for training and 2,356 for testing. The model trained for ten epochs and produced impressive performance measures. Both training and validation accuracy exceeded 98%, confirming the model's capacity to generalise successfully across the dataset. The classification report confirms the model's remarkable performance, with accuracy, recall, and F1-scores averaging 0.99 across both classes. The infected and non-infected classes each had an accuracy of 0.99, a recall of 0.99, and an F1-score of 0.99. The macro and weighted averages hit 0.99, demonstrating the model's resilience and reliability in identifying medical imaging data. These findings highlight the ability of deep learning models to outperform standard diagnostic approaches by producing consistent, objective, and highly accurate outcomes. This study emphasises artificial intelligence's transformational potential in healthcare, particularly in tackling diagnostic issues related to PCOS. By combining an upgraded VGG16 architecture with bespoke layers, the suggested model creates a non-invasive and efficient diagnostic tool that may drastically reduce diagnostic delays and assist medical professionals in decision-making. The model's high accuracy and dependability make it ideal for real-world clinical applications, opening the path for better patient outcomes and healthcare delivery systems worldwide. This study emphasises the need of using AI technology to enhance diagnostic processes, resulting in earlier identification, better treatment planning, and, ultimately, superior quality care for women with PCOS.