Computer-aided classification of prostate cancer grade groups from MRI images using texture features and stacked sparse autoencoder

•One of the pioneer approaches which attempted to classify 3D volumetric prostate cancer lesions into 5 grade groups from MRI images.•Achieved moderate success in classification of 4 grade groups.•The method uses stacked sparse autoencoders (SSAE) to transform low-level texture features extracted fr...

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Bibliographic Details
Published in:	Computerized medical imaging and graphics Vol. 69; pp. 60 - 68
Main Authors:	Abraham, Bejoy, Nair, Madhu S.
Format:	Journal Article
Language:	English
Published:	United States Elsevier Ltd 01.11.2018 Elsevier Science Ltd
Subjects:	Biomarkers CAI Cancer Computer assisted instruction Deep learning Diffusion coefficient Feature extraction Gleason grade Image classification Lesions Magnetic resonance imaging Medical imaging Multiparametric MRI NMR Nuclear magnetic resonance Prostate cancer Stacked sparse autoencoder Texture Texture features Training Deep learning Gleason grade Stacked sparse autoencoder Texture features Prostate cancer Multiparametric MRI
ISSN:	0895-6111, 1879-0771, 1879-0771
Online Access:	Get full text
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