Smartphone-Supported Automated Malaria Parasite Detection.
Yang F, Yu H, Poostchi M, Silamut K, Maude RJ, Jaeger S
SIIM conference on Machine Intelligence in Medical Imaging, 2018.
Abstract:
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. yagWe propose a customized CNN model for parasite classification. Our customized CNN model consists of three convolutional layers, three max pooling layers, two fully connected layers and a softmax classification layer .
Yang F, Yu H, Poostchi M, Silamut K, Maude RJ, Jaeger S. Smartphone-Supported Automated Malaria Parasite Detection.
SIIM conference on Machine Intelligence in Medical Imaging, 2018.