Ensemble of Deep Learning Classifiers and Source Fusion for Improved Malaria Cell Classification

Malaria is an infectious disease caused by Plasmodium parasites, and it can be severe if left untreated. Traditional diagnostic methods are costly and prone to human error. However, computer-aided techniques offer a faster and more accurate way to detect malaria viruses. With the vast amounts of available data, deep learning approaches are particularly suited for malaria classification. This paper introduces a method combining original and filtered data through source fusion, improving classification accuracy by up to 7%. Additionally, it proposes fusing the probabilistic decisions of an ensemble of pre-trained CNN classifiers using both original and filtered data. Extensive experiments conducted on a well-known malaria dataset demonstrate that the proposed approach achieves exceptional classification performance, with 100% accuracy, specificity, and sensitivity.