Deep CNN frameworks comparison for malaria diagnosis
Authors: Priyadarshini Adyasha Pattanaik (INTERMEDIA), Zelong Wang (TSP), Patrick Horain (INTERMEDIA)
Abstract: We compare Deep Convolutional Neural Networks (DCNN) frameworks, namely AlexNet and VGGNet, for the classification of healthy and malaria-infected cells in large, grayscale, low quality and low resolution microscopic images, in the case only a small training set is available. Experimental results deliver promising results on the path to quick, automatic and precise classification in unstained images.
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