Deep CNN frameworks comparison for malaria diagnosis

09/06/2019 ∙ by Priyadarshini Adyasha Pattanaik, et al. ∙ 0 ∙ share

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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Deep CNN frameworks comparison for malaria diagnosis

Authors

An Evolution of CNN Object Classifiers on Low-Resolution Images

TOP-GAN: Label-Free Cancer Cell Classification Using Deep Learning with a Small Training Set

DeepCough: A Deep Convolutional Neural Network in A Wearable Cough Detection System

Image Aesthetics Assessment using Multi Channel Convolutional Neural Networks

Enhancement of Anime Imaging Enlargement using Modified Super-Resolution CNN

Comparative Study of APIs and Frameworks for Haptic Application Development

Diagnosis of Celiac Disease and Environmental Enteropathy on Biopsy Images Using Color Balancing on Convolutional Neural Networks

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