MalariaNet: A Computationally Efficient Convolutional Neural Network Architecture for Automated Malaria Detection

— Despite much progress in detection and treatment, malaria remains one of the most prevalent diseases on earth, both in terms of incidence and death rate. Multiple studies have shown that early detection of malaria is paramount to preventing fatal outcomes; however, current testing methods have notable issues involving cost and accessibility. As a result, deep learning algorithms have been developed for malaria detection and have achieved state of the art results in rapid diagnosis; however, it has been noted that the computational expense of running elaborate models makes deep learning based detection methods inaccessible in remote areas of the world. We develop a computationally efficient, relatively shallow neural network architecture that can diagnose malaria from cell images obtained from thin blood smear slides. Specifically, our algorithm, dubbed MalariaNet, is a 7-layer convolutional neural network trained using the Adaptive Moment Estimation algorithm on the open source NIH malaria dataset, containing 27,588 images of parasitized and uninfected cells. We report that MalariaNet achieves an accuracy of 0.968, F1 score of 0.955, precision of 0.946, and recall of 0.974. We hope that our computationally considerate model inspires more research in producing accessible artificial intelligence solutions for disease detection tasks.