MTANet: Multi-Type Attention Ensemble for Malaria Parasite Detection

Abstract

Malaria is a severe infectious disease caused by the Plasmodium parasite. Diagnosing and treating the disease is crucial to increase the chances of survival. However, detecting malaria parasites is still a manual process performed by experts examining blood smears, especially in less developed countries. This task is time-consuming and prone to errors. Fortunately, deep learning-based object detection methods have shown promising results in automating this task, allowing quick diagnosis and treatment. In this work, we proposed an object detection ensemble architecture, MTANet, that efficiently detects malaria parasite species using one tailored YOLOv5 version integrated with an attention-based approach. We compared its performance against several methods in the literature. The experimental results have shown that MTANet can efficiently and accurately address the detection of different species with a single model.