DEVELOPING A MACHINE LEARNING MODEL FOR MALARIA DIAGNOSIS IN RURAL AREAS

Abstract Medical diagnosis of diseases like Malaria and tuberculosis still use microscopy as a standard, but this procedure is usually very tiring for pathologists and health workers as it imposes much stress on their vision. Due to the fatigue that health workers get from this process, they might end up misdiagnosing a case. In most Rural areas of Cameroon and Ghana, there are no qualified personnel to do these diagnoses. Moreover, according to the World Bank, malaria still kills millions of people every year in Sub-Saharan Africa. To solve this problem, we used a machine learning approach; transfer learning to retrain an already existing model to perform binary classification on malaria blood smear images. The pretrained model was already optimized for devices with low memory, therefore this project’s model can work on low memory devices with no network connectivity. This project also explored Generative Adversarial networks as an alternative way of training a classifier for scenarios with data scarcity. This project shows how a model trained on a different task can be retrained to solve a similar task and shows a technique for developing a classifier in scenarios of data scarcity.