Polycystic Ovary Syndrome (PCOS) is a leading cause of female infertility and is associated with various health complications, including preterm abortions, anovulation, and ovarian cancer. It affects approximately 5% to 10% of women in their reproductive years. PCOS diagnosis often relies on ultrasound imaging to assess ovarian follicle size, count and arrangement. Accurately diagnosing PCOS in clinical practice poses significant challenges for radiologists due to the variability in follicle sizes and their complex relationships with surrounding blood vessels and tissues. This process is labor-intensive, prone to errors, and time-consuming. To address these challenges, numerous research efforts have focused on automating the detection of PCOS-affected ovaries. While advancements have been made, further improvements are needed to enhance diagnostic accuracy. Convolutional Neural Networks (CNNs) have shown promise in PCOS classification, but models relying solely on global features may achieve suboptimal results, as regional features are often overlooked. This paper introduces a feature fusion model named PCOSFusionNet designed to improve the accuracy of PCOS classification. The proposed system combines handcrafted features extracted using the Histogram of Oriented Gradients (HOG) descriptor with global features obtained from the VGG19 deep learning model. Additionally, Contrast Limited Adaptive Histogram Equalization (CLAHE) is applied during preprocessing to enhance image quality and improve feature extraction. The watershed method is employed for segmentation before classification. By integrating deep features with handcrafted features, the system achieves superior classification performance across multiple metrics, including accuracy, precision, recall, and F1-score, using five-fold cross-validation. The performance of the proposed PCOSFusionNet model was evaluated on two publicly available datasets. The first dataset (Dataset_1) contains 3856 ultrasound images and the second dataset (Dataset_2) comprises 12,680 ultrasound images. On these datasets, PCOSFusionNet achieved accuracies of 98.49% and 98.30%, respectively, surpassing existing state-of-the-art methods and demonstrating its effectiveness in PCOS diagnosis.
Keywords: CNN model; feature fusion; image classification; polycystic ovary syndrome (PCOS); ultrasound image of ovary.