Ensemble of Deep Learning Classifiers and Source Fusion for Improved Malaria Cell Classification
Hasan Demırel, Mahmoud Akel, Alaa Eleyan · Preprints.org · 2025
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.