Classification of Malaria-Infected Cells Using Deep Convolutional Neural Networks

Abstract

Malaria is a life-threatening disease caused by parasites that are transmitted to people through the bites of infected mosquitoes. Automation of the diagnosis process will enable accurate diagnosis of the disease and hence holds the promise of delivering reliable health-care to resource-scarce areas. Machine learning technologies have been used for automated diagnosis of malaria. We present some of our recent progresses on highly accurate classification of malaria-infected cells using deep convolutional neural networks. First, we describe image processing methods used for segmentation of red blood cells from wholeslide images. We then discuss the procedures of compiling a pathologists-curated image dataset for training deep neural network, as well as data augmentation methods used to significantly increase the size of the dataset, in light of the overfitting problem associated with training deep convolutional neural networks. We will then compare the classification accuracies obtained by deep convolutional neural networks through training, validating, and testing with various combinations of the datasets. These datasets include the original dataset and the significantly augmented datasets, which are obtained using direct interpolation, as well as indirect interpolation using automatically extracted features provided by stacked autoencoders. This chapter ends with a discussion of further research.

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