Efficient Deep Learning-based Approach For Malaria Detection Using Red Blood Cell Smears

In this article, Hemachandran et al. implemented three deep learning algorithms to detect malaria cases. They compared the performance of CNN, MobileNetV2, and ResNet50 models and found that ResNet50 had the highest accuracy in identifying malaria. The dataset used in the study was obtained from a public repository and contained 27,558 cell images. Preprocessing was done to enhance the performance of the models.