ADVANCING MALARIA IDENTIFICATION FROM MICROSCOPIC BLOOD SMEARS USING HYBRID DEEP LEARNING FRAMEWORKS

Abstract

Malaria, a life-threatening disease transmitted by mosquitoes, remains a major public health challenge, claiming thousands of lives each year. Limited access to reliable detection tools, combined with challenges such as insufficient laboratory resources and inexperienced personnel, contribute to its high mortality rate. Recently, advancements in image analysis of malariainfected red blood cells (RBCs) have provided promising alternatives for more accessible detection methods. By leveraging digital microscopy and innovative machine learning approaches, researchers aim to develop practical solutions that can improve diagnostic accuracy and accessibility. This approach not only enables a faster response in clinical settings but also highlights the potential for integration with IoT-enabled devices, facilitating wider deployment in resource-constrained regions. Such advancements underscore the potential of image-based malaria detection methods to enhance early diagnosis and treatment, especially in areas with limited medical resources.