A Review on Deep Learning-Based Detection of Malaria from Blood Smear Images

Malaria, a mosquito-borne disease caused by Plasmodium parasites, continues to impact millions globally. Traditional diagnostic techniques, particularly microscopic examination of blood smear images, are labor-intensive and prone to human error. In recent years, deep learning, especially Convolutional Neural Networks (CNNs), has significantly advanced the field of automated malaria detection. This review discusses the evolution of malaria diagnosis, applications of various deep learning models, datasets used, evaluation metrics, and the challenges and future directions of AI-driven solutions in this domain. Fifteen significant studies are reviewed to illustrate the landscape of research and deployment in deep learning-based malaria detection.