Journal of Pathology Informatics Journal of Pathology Informatics
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ORIGINAL ARTICLE
Year : 2017  |  Volume : 8  |  Issue : 1  |  Page : 1

Classifications of multispectral colorectal cancer tissues using convolution neural network


1 Laboratory of Conception, Optimization and Modelling of Systems, University of Lorraine, Metz, Lorraine, France; Faculty of Engineering, Lebanese University, Beirut, Lebanon
2 Laboratory of Conception, Optimization and Modelling of Systems, University of Lorraine, Metz, Lorraine, France; Laboratory for Imagery, Vision and Artificial Intelligence, École de Technologie Supérieure, Montréal, Québec, Canada
3 Faculty of Engineering, Lebanese University, Beirut, Lebanon
4 Laboratory for Imagery, Vision and Artificial Intelligence, École de Technologie Supérieure, Montréal, Québec, Canada
5 Laboratory of Conception, Optimization and Modelling of Systems, University of Lorraine, Metz, Lorraine, France

Correspondence Address:
Ahmad Chaddad
Laboratory of Conception, Optimization and Modelling of Systems, University of Lorraine, Metz, Lorraine

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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/jpi.jpi_47_16

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Background: Colorectal cancer (CRC) is the third most common cancer among men and women. Its diagnosis in early stages, typically done through the analysis of colon biopsy images, can greatly improve the chances of a successful treatment. This paper proposes to use convolution neural networks (CNNs) to predict three tissue types related to the progression of CRC: benign hyperplasia (BH), intraepithelial neoplasia (IN), and carcinoma (Ca). Methods: Multispectral biopsy images of thirty CRC patients were retrospectively analyzed. Images of tissue samples were divided into three groups, based on their type (10 BH, 10 IN, and 10 Ca). An active contour model was used to segment image regions containing pathological tissues. Tissue samples were classified using a CNN containing convolution, max-pooling, and fully-connected layers. Available tissue samples were split into a training set, for learning the CNN parameters, and test set, for evaluating its performance. Results: An accuracy of 99.17% was obtained from segmented image regions, outperforming existing approaches based on traditional feature extraction, and classification techniques. Conclusions: Experimental results demonstrate the effectiveness of CNN for the classification of CRC tissue types, in particular when using presegmented regions of interest.


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