CystNet: An AI driven model for PCOS detection using multilevel thresholding of ultrasound images

Abstract

Abstract Polycystic Ovary Syndrome (PCOS) is a widespread endocrinological dysfunction impacting women of reproductive age, categorized by excess androgens and a variety of associated syndromes, consisting of acne, alopecia, and hirsutism. It involves the presence of multiple immature follicles in the ovaries, which can disrupt normal ovulation and lead to hormonal imbalances and associated health complications. Routine diagnostic methods rely on manual interpretation of ultrasound (US) images and clinical assessments, which are time-consuming and prone to errors. Therefore, implementing an automated system is essential for streamlining the diagnostic process and enhancing accuracy. By automatically analyzing follicle characteristics and other relevant features, this research aims to facilitate timely intervention and reduce the burden on healthcare professionals. The present study proposes an advanced automated system for detecting and classifying PCOS from ultrasound images. Leveraging Artificial Intelligence (AI) based techniques, the system examines affected and unaffected cases to enhance diagnostic accuracy. The pre-processing of input images incorporates techniques such as image resizing, normalization, augmentation, Watershed technique, multilevel thresholding, etc. approaches for precise image segmentation. Feature extraction is facilitated by the proposed CystNet technique, followed by PCOS classification utilizing both fully connected layers with 5-fold cross-validation and traditional machine learning classifiers. The performance of the model is rigorously evaluated using a comprehensive range of metrics, incorporating AUC score, accuracy, specificity, precision, F1-score, recall, and loss, along with a detailed confusion matrix analysis. The model demonstrated a commendable accuracy of $$96.54\%$$ when utilizing a fully connected classification layer, as determined by a thorough 5-fold cross-validation process. Additionally, it has achieved an accuracy of $$97.75\%$$ when employing an ensemble ML classifier. This proposed approach could be suggested for predicting PCOS or similar diseases using datasets that exhibit multimodal characteristics.

Keywords

• Polycystic ovary syndrome
• Follicles
• Image classification
• Convolutional autoencoder
• EfficientNet
• Image segmentation