A CNN-Based Malaria Diagnosis from Blood Cell Images with SHAP and LIME Explainability

TL;DR

This paper presents a CNN-based method for malaria diagnosis from blood cell images, achieving high accuracy and incorporating explainability techniques like SHAP and LIME to improve interpretability in resource-limited settings.

Contribution

It introduces a custom CNN model for malaria detection and applies explainability methods to enhance understanding of the model's decisions.

Findings

CNN achieves 96% accuracy in classifying blood cells

Explainability techniques improve model interpretability

Compared with established architectures, the custom CNN performs competitively

Abstract

Malaria remains a prevalent health concern in regions with tropical and subtropical climates. The cause of malaria is the Plasmodium parasite, which is transmitted through the bites of infected female Anopheles mosquitoes. Traditional diagnostic methods, such as microscopic blood smear analysis, are low in sensitivity, depend on expert judgment, and require resources that may not be available in remote settings. To overcome these limitations, this study proposes a deep learning-based approach utilizing a custom Convolutional Neural Network (CNN) to automatically classify blood cell images as parasitized or uninfected. The model achieves an accuracy of 96%, with precision and recall scores exceeding 0.95 for both classes. This study also compares the custom CNN with established deep learning architectures, including ResNet50, VGG16, MobileNetV2, and DenseNet121. To enhance model interpretability, Explainable AI techniques such as SHAP, LIME, and Saliency Maps are applied. The proposed system shows how deep learning can provide quick, accurate and understandable malaria diagnosis, especially in areas with limited resources.