WomenCare AI: Comparative Analysis and Optimization of Image Processing Techniques for Ovarian Condition Classification
Keywords:
Automated diagnosis, Clinical decision support, Deep learning, EfficientNet, Grad-CAM, Healthcare AI, Medical image processing, OpenCV, Ovarian condition classification, PCOS detection, ResNet50, Transfer learningAbstract
Ovarian health assessment remains a significant challenge in modern medical diagnostics, where early detection directly influences treatment success and patient outcomes. Medical imaging plays a vital role in identifying ovarian abnormalities; however, variations in image quality, noise presence, and low contrast often hinder accurate interpretation. Within the paradigm of intelligent healthcare systems, the WomenCare AI system is conceptualized as a deep learning-driven framework that enables automated classification of ovarian conditions through integrated image processing and neural network analysis. Traditional diagnostic approaches depend on manual evaluation, which introduces delays and subjective inconsistencies in classification outcomes. The proposed framework utilizes OpenCV-based preprocessing methods, including noise reduction, normalization, contrast enhancement, and image resizing to improve data consistency and structural integrity of ultrasound images. It further incorporates ResNet50 and EfficientNet models to extract deep features and perform comparative analysis based on accuracy, efficiency, and reliability. The dataset comprises 11,784 ovarian ultrasound images, including 6,784 infected and 5,000 non-infected cases sourced from a publicly available FigShare repository. Experimental evaluation demonstrates that the proposed WomenCare AI system achieves the highest classification performance with an accuracy of 95.31%, precision of 93.06%, recall of 94.37%, and F1 score of 93.71%, outperforming baseline models including ResNet50, VGG19, DenseNet121, MobileNetV2, and Modified Vision Transformer. The integration of Grad-CAM visualization further enhances model interpretability by highlighting clinically relevant regions in ultrasound images. The proposed approach contributes toward improving clinical decision support systems by delivering faster, consistent, and scalable diagnostic assistance while advancing research in AI-based healthcare solutions.
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