MalrENSNet: a multi-model ensemble approach for detection of malaria from thin-blood smear images

Abstract Malaria is caused by Plasmodium parasites transmitted through the bites of infected female Anopheles mosquitoes, and remains a significant global health issue. Accurate and timely diagnosis of malaria is essential for effective treatment, yet conventional microscopic examination remains prone to human error and delay. The proposed study introduces an innovative multi-model ensemble approach that integrates the pre-trained backbone models MobileNetV2 and EfficientNetB0 networks to enhance malaria parasite detection performance. The preprocessing steps were applied to the images for outlier detection and removal before being fed into the proposed model to achieve an impressive accuracy of 99%. The proposed ensemble model outperformed various pre-trained Deep Learning (DL) models and state-of-the-art techniques. The model was evaluated using thin-blood smear images from the publicly available NIH Malaria dataset with 27,558 images, demonstrating its potential for application in clinical settings and contributing to more efficient and accurate malaria parasite detection using blood cell smear images. Further, the proposed model was deployed in a Python-based web application to improve user interaction.