Enhancing Disease Diagnosis Using Explainable AI: A Deep Learning Approach
Abstract
The integration of Artificial Intelligence (AI) in healthcare has significantly improved disease diagnosis, yet challenges remain in model interpretability. This study explores explainable AI (XAI) techniques for enhancing deep learning models in disease detection, focusing on conditions such as cancer, cardiovascular diseases, and neurological disorders. We employ convolutional neural networks (CNNs) alongside SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model-agnostic Explanations) to improve model transparency and physician trust. The results demonstrate improved accuracy and interpretability, allowing medical professionals to make informed decisions with AI-assisted diagnostics.
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