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An Analysis in Tissue Classification for Colorectal Cancer Histology Using Convolution Neural Network and Colour Models

An Analysis in Tissue Classification for Colorectal Cancer Histology Using Convolution Neural Network and Colour Models

Shamik Tiwari
Copyright: © 2018 |Volume: 9 |Issue: 4 |Pages: 19
ISSN: 1947-8186|EISSN: 1947-8194|EISBN13: 9781522545118|DOI: 10.4018/IJISMD.2018100101
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MLA

Tiwari, Shamik. "An Analysis in Tissue Classification for Colorectal Cancer Histology Using Convolution Neural Network and Colour Models." IJISMD vol.9, no.4 2018: pp.1-19. http://doi.org/10.4018/IJISMD.2018100101

APA

Tiwari, S. (2018). An Analysis in Tissue Classification for Colorectal Cancer Histology Using Convolution Neural Network and Colour Models. International Journal of Information System Modeling and Design (IJISMD), 9(4), 1-19. http://doi.org/10.4018/IJISMD.2018100101

Chicago

Tiwari, Shamik. "An Analysis in Tissue Classification for Colorectal Cancer Histology Using Convolution Neural Network and Colour Models," International Journal of Information System Modeling and Design (IJISMD) 9, no.4: 1-19. http://doi.org/10.4018/IJISMD.2018100101

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Abstract

Computer vision-based identification of different tissue categories in histological images is a critical application of the computer-assisted diagnosis (CAD). Computer-assisted diagnosis systems support to reduce the cost and increase the efficiency of this process. Traditional image classification approaches depend on feature extraction methods designed for a specific problem based on domain information. Deep learning approaches are becoming important alternatives with advance of machine learning technologies to overcome the numerous difficulties of the feature-based approaches. A method for the classification of histological images of human colorectal cancer containing seven different types of tissue using convolutional neural network (CNN) is proposed in this article. The method is evaluated using four different colour models in absence and presence of Gaussian noise. The highest classification accuracies are achieved with HVI colour model, which is 95.8% in nonexistence and 78.5% in existence of noise respectively.

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