<![CDATA[Polycystic Ovarian Syndrome (PCOS) is a hormone-related health condition in women, commonly classified as an endocrine disorder. It is most prevalent during the childbearing years, typically between the ages of 15 and 44. PCOS leads to hormonal imbalances that cause irregular menstrual cycles, hair loss, and other symptoms, and it is associated with long-term health risks such as heart disease and diabetes. Recent advances in deep learning have shown promising results in accurately recognizing and differentiating ovarian cysts from other ovarian tumours. This study proposes a novel technique for PCOS symptom detection by analysing ovarian images through feature extraction, classification, and metaheuristic-based optimization. Ovarian images are first pre-processed for noise removal and smoothing, followed by feature extraction and classification using a Convolutional Wavelet Attention Neural Network with a Naïve Bayes Fuzzy Autoencoder (CWANN–NBFA). Optimization is then performed using the Metaheuristic Multilevel Hawks Algae Optimization (MMHAO) algorithm. Experimental evaluations were conducted on multiple ovarian image datasets. The proposed technique achieved an accuracy of over 98% across the PCOSUSG, KFHU, and MMOTU datasets, demonstrating its robustness and effectiveness in addressing the challenges of PCOS detection.]]>
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