Automating Malaria Diagnosis with XAI: Using Deep-Learning Technologies for More Accurate, Efficient, and Transparent Results

Abstract

Malaria is a deadly infectious disease that claims numerous lives worldwide each year, primarily due to delayed or incorrect diagnosis using the manual microscope. This article proposes the automation of the diagnosis process through deep-learning technologies, specifically convolutional neural networks (CNNs), based on the intensity characteristics of Plasmodium parasites and erythrocytes. The approach involves feeding images into CNN models such as ResNet50, CNN, and MobileNet, with the MobileNet model achieving the best overall performance. The first novelty of this paper is that we update the pre-trained models which give us better results. To further enhance the system, the article advocates for the use of explainable artificial intelligence (XAI) techniques, including feature attribution and counterfactual explanations, to improve the accuracy, efficiency, and transparency of the malaria diagnosis system. The proposed system integrates deep learning and XAI, which can provide clear and interpretable explanations for decision-making processes, guide the development of more effective diagnostic tools and save lives. For instance, we use Grad-CAM and Grad-CAM++ which counter the affected areas on the images and that could be a noble contribution to this paper. It is shown via extensive performance study that auto-mating the process can accurately and efficiently detect the malaria parasite in blood samples with a sensitivity of over 95% and less complexity than prior methods reported in the literature.