An Explainable Deep Learning Framework for Intelligent Medical Diagnosis and Transparent Clinical Decision Support
Keywords:
Clinical decision support system, Convolutional neural network, Deep learning, Explainable artificial intelligence (XAI), Grad-CAM, Healthcare AI, Medical diagnosis, SHAPAbstract
Artificial intelligence (AI) has become an essential technology in modern healthcare, particularly in medical image analysis and clinical decision support systems. Despite the remarkable diagnostic performance of deep learning models, their black-box nature limits clinical adoption due to the lack of interpretability and transparency. To address this challenge, this study proposes an explainable deep learning framework (XDLF) for intelligent medical diagnosis by integrating a modified ResNet-50 architecture with explainable artificial intelligence (XAI) techniques, including gradient-weighted class activation mapping (Grad-CAM) and SHapley Additive exPlanations (SHAP). The proposed framework was evaluated using publicly available medical imaging datasets comprising chest X-ray, histopathology, and COVID-19 radiography images. Comprehensive preprocessing techniques, including normalization, noise reduction, contrast enhancements, and data augmentation, were employed to improve model robustness and generalization capability. Experimental results demonstrate that the proposed framework achieved superior diagnostic performance with 96.8% accuracy, 95.9% precision, 96.3% recall, and 96.1% F1-score, outperforming conventional CNN-based models. Furthermore, the explainability module successfully identified clinically relevant regions and quantified feature contributions, thereby improving interpretability and physician trust in AI-assisted diagnosis. The proposed XDLF provides a transparent, reliable, and scalable clinical decision support framework suitable for future real-world healthcare applications.
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