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

TL;DR

M2ANET is a hybrid mobile deep learning model combining local feature extraction and global attention to accurately and efficiently classify malaria parasites in blood cell images, suitable for resource-limited settings.

Contribution

This paper introduces M2ANET, a novel hybrid mobile network that integrates MobileNetV3 blocks with a modified multi-head self-attention mechanism for malaria detection.

Findings

M2ANET outperforms existing lightweight models in accuracy and efficiency.

The model demonstrates potential for deployment in resource-constrained healthcare environments.

Extensive experiments validate the effectiveness of the hybrid approach.

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, 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.