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<p>Malaria is a life-threatening infectious disease caused by <i>Plasmodium</i> parasites and can become severe if not diagnosed and treated promptly. Conventional diagnostic methods are often time-consuming, costly, and susceptible to human error. Computer-aided diagnostic systems provide a faster and more objective alternative for detecting malaria parasites from microscopic images. With the growing availability of medical imaging data, artificial intelligence–based approaches have become increasingly suitable for automated malaria classification. This study proposes a structured two-level fusion framework for binary malaria cell classification. At the first level, source fusion integrates original, Gaussian-filtered, and median-filtered images to enhance feature diversity. At the second level, decision fusion combines probabilistic outputs from an ensemble of Vision Transformer and Convolutional Neural Network architectures using sum rule, product rule, and majority voting strategies. The classification stage employs Vision Transformer Tiny, GhostNet100, and Mobile Vision Transformer Extra Small models. The contribution of this work lies in the systematic hierarchical integration of source-level diversity and structured probabilistic decision aggregation across heterogeneous lightweight architectures. Experimental results demonstrate that the proposed approach improves classification robustness and performance. The product rule within the two-level fusion framework achieves superior performance, reaching 98.9% accuracy, 99.4% positive predictive value, and 99.6% specificity.</p>

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Ensemble of CNN and vision transformer models for fusion-based malaria cell classification

  • 期刊论文

  • 发表日期：

    2026年3月16日

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  • Mahmoud Akel,
  • Doğu Manalı,
  • Alaa Eleyan,
  • Hasan Demirel

摘要

Malaria is a life-threatening infectious disease caused by Plasmodium parasites and can become severe if not diagnosed and treated promptly. Conventional diagnostic methods are often time-consuming, costly, and susceptible to human error. Computer-aided diagnostic systems provide a faster and more objective alternative for detecting malaria parasites from microscopic images. With the growing availability of medical imaging data, artificial intelligence–based approaches have become increasingly suitable for automated malaria classification. This study proposes a structured two-level fusion framework for binary malaria cell classification. At the first level, source fusion integrates original, Gaussian-filtered, and median-filtered images to enhance feature diversity. At the second level, decision fusion combines probabilistic outputs from an ensemble of Vision Transformer and Convolutional Neural Network architectures using sum rule, product rule, and majority voting strategies. The classification stage employs Vision Transformer Tiny, GhostNet100, and Mobile Vision Transformer Extra Small models. The contribution of this work lies in the systematic hierarchical integration of source-level diversity and structured probabilistic decision aggregation across heterogeneous lightweight architectures. Experimental results demonstrate that the proposed approach improves classification robustness and performance. The product rule within the two-level fusion framework achieves superior performance, reaching 98.9% accuracy, 99.4% positive predictive value, and 99.6% specificity.