The self- upgrading mobile application for the automatic malaria detection

Abstract

WHO set an ambitious vision of malaria control which includes reducing malaria case incidence by 90% by 2030. Many tools and approaches have been considered to enable the progress toward malaria vision. The use of portable smartphones and machine learning (ML) software is a promising one among them. Recently, many ML models have been proposed for malaria detection. From the interview with health workers in the field, we realized ML model should be continuously improved to provide higher accuracy and/or more capability to cover practical issues found in a real setting such as malaria mosquitoes with drug resistance. In this paper, we propose the mobile application for malaria detection which upgrades ML model on its own without depending on internet connection. Unavailability of internet connection is commonly observed in malaria epidemic countries. We also learned that ML model should be not only accurate but also resource-efficient. This motivated us to set up performance metrics for ML model. Based on the metrics, we chose the optimal ML model of Resnet-50. While most of the prior art ML models were optimized in terms of accuracy only, the optimal model of our choice satisfies both accuracy and resource efficiency. With adopting the model, we architect self-upgrading malaria-detection application and it is validated using ATAM (Architecture Tradeoff Analysis Method) to ensure the application works in the resource-constrained setting as desired. Lastly, we develop the prototype application and show it diagnoses malaria parasites as expected. To collect more blood samples and feedback from prospect users, we plan to do testing at local clinics in India and Myanmar.