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Automated Parasite Classification of Malaria on Thick Blood Smears.
According to the WHO malaria report in 2017, an estimated 216 million malaria cases were detected in 2016, causing approximately 445,000 deaths. Microscopy is the gold standard for malaria diagnosis.Thick blood smears are used to detect the presence of malaria parasites; Thin blood smears are used to differentiate parasite species. Microscopy examination is of low cost and is widely available, but is timeconsuming, and the effectiveness of microscopy diagnosis depends on the parasitologists’ expertise. We propose a customized convolutional neural network (CNN) model including three convolutional layers, two fully-connected layers and a softmax classification layer. Following each convolutional layer, a batch normalization layer, an activation layer, and a max-pooling layer are introduced to select feature subsets.