M2ANET: Mobile Malaria Attention Network for efficient classification of plasmodium parasites in blood cells

Abstract

Malaria is a life-threatening infectious disease caused by Plasmodium parasites, which poses a significant public health challenge worldwide, particularly in tropical and subtropical regions. Timely and accurate detection of malaria parasites in blood cells is crucial for effective treatment and control of the disease. In recent years, deep learning techniques have demonstrated remarkable success in medical image analysis tasks, offering promising avenues for improving diagnostic accuracy. However, limited studies focus on hybrid mobile models due to the complexity of combining two distinct architectures and the significant memory demand of self-attention mechanisms, especially for edge devices. In this study, we introduce  (Mobile Malaria Attention Network), a hybrid model integrating MBConv3 (MobileNetV3 blocks) for efficient local feature extraction and a modified global-MHSA (multi-head self-attention) mechanism for capturing global context in blood cell images. Experimental results on the Malaria Cell Images Dataset show that  achieves a top-1 accuracy of 95.45% and a Cohen Kappa score of 0.91, outperforming some state-of-the-art lightweight and mobile networks. These results highlight its effectiveness and efficiency for malaria diagnosis. The development of  demonstrates the potential of hybrid mobile models for improving malaria diagnosis in resource-constrained settings.