  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.
