Deep Convolutional Neural Network Ensemble for Improved Malaria Parasite Detection

Malaria prognosis, performed through the identification of parasites using microscopy, is a vital step in the early initiation of treatment. Malaria inducing parasites such as Plasmodium falciparum are difficult to identify and thus have a high mortality rate. For these reasons, a deep convolutional neural network algorithm is proposed in this paper to aid in accurately identifying parasitic cells from red blood smears. By using a mixture of machine learning techniques such as transfer learning, a cyclical and constant learning rate, and ensemble methods, we have developed a model capable of accurately identifying parasitic cells within red blood smears. 14 networks pretrained from the ImageNet database are retrained with the fully connected layers replaced. A cyclical and constant learning rate are used to traverse local minima in each network. The output of each trained neural network is representing a single vote that is used in the classification process. Majority voting criteria are applied in the final classification decision between the candidate malaria cells. Several experiments were conducted to evaluate the performance of the proposed model. The NIH Malaria Dataset from the National Institute of Health, a dataset of 27,558 images formed from microscopic patches of red blood smears, is used in these experiments. The dataset is segmented into 80% training set, 10% validation set, and 10% test set. The validation set is used as the decision metric for choosing ensemble network architectures and the test set is used as the evaluation metric for each model. Different ensemble network architectures are experimented with and promising performance is observed on the test dataset with the best models achieving a test accuracy better than several state-of-the-art methodologies.