Enhancing Medical Image Diagnosis Accuracy Using Hybrid Convolutional Neural Networks and Explainable AI Techniques

Medical image diagnosis has witnessed significant advancement through deep learning technologies, particularly Convolutional Neural Networks (CNNs). However, the black-box nature of traditional CNNs poses challenges in clinical adoption where interpretability is crucial. This study presents a novel hybrid CNN architecture integrated with Explainable AI (XAI) techniques to enhance diagnostic accuracy while maintaining interpretability in medical imaging applications. We developed a multi-scale feature extraction framework combining residual connections, attention mechanisms, and gradient-based visualization techniques. The proposed methodology was evaluated on chest X-ray, dermatological, and retinal fundus datasets, achieving superior performance with 94.7% accuracy for pneumonia detection, 92.3% for skin cancer classification, and 91.8% for diabetic retinopathy grading. The integration of LIME, GradCAM, and SHAP visualization techniques provided clinically meaningful explanations, improving physician trust and decision-making confidence by 23.4%. Results demonstrate that hybrid CNN architectures with XAI integration significantly outperform traditional approaches while maintaining computational efficiency and clinical interpretability requirements.