Survey of ConvNeXt as a Cutting-Edge Approach in Detecting Polycystic Ovary Syndrome with Advanced Image Analysis

A thorough review examines ConvNeXt, a novel image analysis method for PCOS detection. The main goal is to test ConvNeXt's ability to diagnose PCOS via ultrasound and magnetic resonance imaging. This survey emphasizes the model's architecture improvements and deep learning-based feature extraction and categorization. Comparing ConvNeXt against old methods and modern deep learning models is the goal. ConvNeXt seems to be more accurate and efficient in PCOS identification, suggesting therapeutic applicability. This survey highlights gaps in the literature and suggests future research directions, emphasizing the need for larger datasets and robust validation protocols to improve diagnostic precision and enable PCOS management with advanced imaging techniques. Kaggle dataset performance measures include Accuracy, Precision, Recall, F1 Score, AUC-ROC, Training Time (hours), Inference Time (milliseconds per image). Five ConvNeXt architectures were found. ConvNeXt-Tiny: 0.92. 0.89, 0.91, 0.90, 0.95, 12, 15, Small: 0.93, 0.90, 0.92, 0.91, 0.96, 16, 18, Base: 0.94, 0.91, 0.93, 0.92, 0.97, 22, 25, Large: 0.95, 0.92, 0.94, 0.93, 0.98, 30, 30, and XL: 0.96, 0.93, 0.95, 0.94, 0.99, 40, 35.