Arquitetura de microserviços para detecção e classificação de malária

Abstract

Malaria affects millions of people each year, predominantly in resourcelimited countries. According to the World Health Organization (WHO 2022), there were an estimated 619,000 Malaria deaths globally in 2021 compared to 625,000 in 2020. Thus, automated classification of Malaria-infected blood smear images is a critical component in improving the efficiency and accuracy of Malaria diagnosis. With the aim of enabling a flexible solution that would allow the integration of neural networks to treat diseases on a large scale, we propose a methodology for Malaria disease classification. Our approach involves data collection, preprocessing and YOLO object detection (in realtime), encapsulated into a microservices environment, creating a modular and scalable system that efficiently handles inference requests while ensuring flexibility and maintainability (as shown in the load test experiments). Thus, we are bridging the gap between fighting Malaria and technology and making an application that could serve as a blueprint for future works in the disease classification field.