Majority-LCL: Towards Malaria Cell Detection using Label Contrastive Learning and Majority voting Ensembling

Abstract

Malaria is a contagious disease caused by Plasmodium, a group of single-celled parasites, and is most commonly transmitted by an infected female Anopheles mosquito. More than 40 percent of the global population is at risk, with approximately 219 million reported cases and around 435000 deaths recorded in 2017 alone. Despite the availability of several advanced diagnostic tools, accurate malaria diagnosis remains challenging in resource-constrained settings, where microscopists often struggle to improve diagnostic accuracy. Deep learning-based cell image classification helps reduce incorrect diagnostic conclusions by enabling automated analysis. This research aims to improve diagnostic accuracy by classifying malaria-infected cells using a majority voting ensemble framework combined with triplet loss aided label contrastive learning. Experimental results demonstrate the effectiveness of the proposed method on microscopic cell images in terms of accuracy, precision, recall, and other evaluation metrics.