Malaria Parasite Detection in Microscopic Blood Smear Images using Deep Learning Approach

Abstract

Malaria remains a significant global health concern, posing formidable challenges to healthcare systems. Conventional diagnostic methods rely on manual examination of blood smears under a microscope, a process prone to inefficiencies and subjectivity. Despite prior attempts to leverage Deep Learning algorithms for malaria diagnosis, practical performance has often fallen short. This paper presents a novel machine learning model centred on Convolutional Neural Networks (CNNs) designed to automate the classification and prediction of infected cells in thin blood smears on standard microscope slides. Through rigorous ten-fold cross-validation with 27,558 single-cell images. This paper reviews various image processing techniques employed for the detection of malaria infection in humans, presenting a comparative analysis of these methods