An Efficient Deep Learning Approach for Malaria Parasite Detection in Microscopic Images

Background: Malaria is a life-threatening disease spread by infected mosquitoes, affecting both humans and animals. Its symptoms range from mild to severe, including fever, muscle discomfort, coma, and kidney failure. Accurate diagnosis is crucial but challenging, relying on expert technicians to examine blood smears under a microscope. Conventional methods are inefficient, while machine learning approaches struggle with complex tasks and require extensive feature engineering. Deep learning, however, excels in complex tasks and automatic feature extraction. Objective: This paper presents EDRI, which is a novel hybrid deep learning model that integrates multiple architectures for malaria detection from red blood cell images. The EDRI model is designed to capture diverse features and leverage multi-scale analysis. Methods: The proposed EDRI model is trained and evaluated on the NIH Malaria dataset comprising 27,558 labeled microscopic red blood cell images. Results: Experiments demonstrate its effectiveness, achieving an accuracy of 97.68% in detecting malaria, making it a valuable tool for clinicians and public health professionals. Conclusions: The results demonstrate the effectiveness of proposed model's ability to detect malaria parasite from red blood cell images, offering a robust tool for rapid and reliable malaria diagnosis.