SHAP-driven explicability of CNN-based computer-aided diagnosis for malaria

Abstract

Convolutional Neural Networks (CNN) hold great promise for medical image classification, but their clinical adoption depends on model reliability. In this study, we leverage Shapley Additive Explanations (SHAP) to explain a CNN-based Computer-Aided Diagnosis (CAD) system for malaria detection from microscopy images. We employ SHAP’s Gradient Explainer to highlight key pixel regions driving the CAD model predictions. By providing transparent, pixel-level insights, this approach empowers healthcare professionals to understand system decisions and enhances trust in automated malaria diagnosis.