Bridging The Gap Between Fighting Diseases and Technology: System Development For Malaria Diagnostics

Malaria remains a major global health challenge, particularly in resource-limited countries, as shown in the World Health Organization reports. Thus, automated classification of Malaria-infected blood smear images is a critical component in improving the efficiency and accuracy of Malaria diagnosis. In this study, we propose a methodology for Malaria disease classification using the YOLO (You Only Look Once) algorithm and developed a microservices architecture using Golang language. With the aim of enabling a flexible solution that would allow the integration of neural networks to treat diseases on a large scale, we designed a modular and scalable system that efficiently handles inference requests while ensuring flexibility and maintainability (as shown in the load test experiment), bridging the gap between fighting Malaria and technology, making an application that could serve as a Blueprint for future works in disease classification field.