Document Type
Article
Rights
Available under a Creative Commons Attribution Non-Commercial Share Alike 4.0 International Licence
Disciplines
2. ENGINEERING AND TECHNOLOGY
Abstract
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 unstrained images.
DOI
http://doi.org10.21427/t6bj-zr14
Recommended Citation
Pattanaik, P, Wang, Z. & Horain, P. (2019). Deep CNN frameworks comparison for malaria diagnosis. IMVIP 2019: Irish Machine Vision & Image Processing, Technological University Dublin, 28-30 August. doi:10.21427/t6bj-zr14
Publication Details
IMVIP 2019: Irish Machine Vision and Image Processing, Technological University Dublin, 28-30 August.