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Efficacy of deep convolutional neural network features on histological manifold for robust breast carcinoma detection
Author(s): Subhankar Chattoraj; Souvik Pratiher; Rajdeep Mukherjee; Saikat Ghosh; Arnab Chakraborty; Diptiman Hazra; Sawon Pratiher
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Paper Abstract

In this study, exploratory deep feature engineering using convolutional neural network (CNN) on histological manifold has been proposed for robust breast carcinoma detection. A comparative evaluation emphasizing the adequacy of manifold learning and CNN aided deep features over state-of-the-art biomarkers and other deep learning models is done for histopathological image (HI) classification. The proposed framework efficiently differentiate the spatial textural non-stationarity in HI and apprehend the topographic aberrations of cancerous tissues and exemplifies its competency for clinical settings deployment in developing countries. Experimental results are discussed in detail.

Paper Details

Date Published: 1 November 2018
PDF: 6 pages
Proc. SPIE 10820, Optics in Health Care and Biomedical Optics VIII, 108203Q (1 November 2018); doi: 10.1117/12.2505522
Show Author Affiliations
Subhankar Chattoraj, Techno India Univ. (India)
Souvik Pratiher, KIIT Univ. (India)
Rajdeep Mukherjee, Manipal Univ. (India)
Saikat Ghosh, Institute of Engineering and Management (India)
Arnab Chakraborty, RCC Institute of Information Technology (India)
Diptiman Hazra, The Univ. of Texas at Arlington (United States)
Sawon Pratiher, Indian Institute of Technology Kharagpur (India)

Published in SPIE Proceedings Vol. 10820:
Optics in Health Care and Biomedical Optics VIII
Qingming Luo; Xingde Li; Ying Gu; Yuguo Tang, Editor(s)

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