A deep architecture based on attention mechanisms for effective end-to-end detection of early and mature malaria parasites in a realistic scenario

Background: Malaria is a critical and potentially fatal disease caused by the Plasmodium parasite and isresponsible for more than 600,000 deaths globally. Early and accurate detection of malaria parasites is crucialfor effective treatment, yet conventional microscopy faces limitations in variability and efficiency.Methods: We propose a novel computer-aided detection framework based on deep learning and attentionmechanisms, extending the YOLO-SPAM and YOLO-PAM models. Our approach facilitates the detection andclassification of malaria parasites across all infection stages and supports multi-species identification.Results: The framework was evaluated on three publicly available datasets, demonstrating high accuracyin detecting four distinct malaria species and their life stages. Comparative analysis against state-of-the-artmethodologies indicates significant improvements in both detection rates and diagnostic utility.Conclusion: This study presents a robust solution for automated malaria detection, offering valuable supportfor pathologists and enhancing diagnostic practices in real-world scenarios.