Enhanced Malaria Detection Using Convolutional Neural Networks with SHAP and LIME for Model Interpretability

Malaria is a major global health concern in low-resource countries. Artificial intelligence (AI), particularly convolutional neural networks (CNNs), has become an effective approach for the automatic identification of malaria in blood smear images. However, the lack of interpretability in AI models limits their widespread adoption in clinical practice. This research introduces an innovative approach combining CNNs for malaria detection with two explainable AI (XAI) methods: SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations). This dual method improves the transparency of the network's decision-making procedure, ensuring that both medical experts and patients can understand the reasoning behind predictions. Our model achieved a 97.8% accuracy in detecting malaria, highlighting its effectiveness and reliability. Through SHAP and LIME, we provide visual explanations of model decisions, making AI-driven tools more interpretable and reliable.