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, with limited studies on
hybrid mobile models due to the complexity of combining two distinct models and
the significant memory demand of self-attention mechanism especially for edge
devices. In this study, we explore the potential of designing a hybrid mobile
model for efficient classification of plasmodium parasites in blood cell
images. Therefore, we present M2ANET (Mobile Malaria Attention Network). The
model integrates MBConv3 (MobileNetV3 blocks) for efficient capturing of local
feature extractions within blood cell images and a modified global-MHSA
(multi-head self-attention) mechanism in the latter stages of the network for
capturing global context. Through extensive experimentation on benchmark, we
demonstrate that M2ANET outperforms some state-of-the-art lightweight and
mobile networks in terms of both accuracy and efficiency. Moreover, we discuss
the potential implications of M2ANET in advancing malaria diagnosis and
treatment, highlighting its suitability for deployment in resource-constrained
healthcare settings. The development of M2ANET represents a significant
advancement in the pursuit of efficient and accurate malaria detection, with
broader implications for medical image analysis and global healthcare
initiatives.

通过结合 MBConv3 和改进的全局 MHSA 机制，我们设计了 M2ANET 模型，用于在血细胞图像中高效分类疟原虫，通过对基准测试的广泛实验，我们证明了 M2ANET 在准确性和效率方面优于一些先进的轻量级和移动网络，同时讨论了 M2ANET 在推动疟疾诊断和治疗方面的潜在影响，并强调了它在资源有限的医疗保健环境中部署的适用性，M2ANET 的开发代表了在高效准确的疟疾检测追求中的重大进展，对医学图像分析和全球医疗保健倡议具有广泛的影响。