Enhancing Medical Image Diagnosis Accuracy Using Hybrid Convolutional Neural Networks and Explainable AI Techniques
Year: 2025
Type: article
Abstract: 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.
Author: Dr. Santosh Kumar Jha
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4412603886