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Malaria is a life-threatening disease that is spread by the Plasmodium parasites. It is detected by trained microscopists who analyze microscopic blood smear images. Modern deep learning techniques may be used to do this analysis automatically. The need for the trained personnel can be greatly reduced with the development of an automatic accurate and efficient model. In this article, we propose an entirely automated Convolutional Neural Network (CNN) based model for the diagnosis of malaria from the microscopic blood smear images. Our deep learning-based model can detect malarial parasites from microscopic images with an accuracy of 96.52%. For practical validation of model efficiency, we have deployed the miniaturized model in a server-backed web application. Data gathered from this environment show that the model can be used to perform inference under 1’s per sample in online (web application) mode, thus engendering confidence that such models may be deployed for efficient practical inferential systems.
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