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MalariaNet: A Computationally Efficient Convolutional Neural Network Architecture for Automated Malaria Detection
2020
International Journal of Engineering Research and
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
doi:10.17577/ijertv9is120158
fatcat:vuecffgikbdpjnzusxy5pbwxve