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Article

Two Ensemble-CNN Approaches for Colorectal Cancer Tissue Type Classification

1
Department of Innovation Engineering, University of Salento, 73100 Lecce, Italy
2
Univ. Polytechnique Hauts-de-France, Univ. Lille, CNRS, Centrale Lille, UMR 8520—IEMN, F-59313 Valenciennes, France
3
Institute of Applied Sciences and Intelligent Systems, National Research Council of Italy, 73100 Lecce, Italy
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Gonzalo Pajares Martinsanz
Received: 23 January 2021 / Revised: 16 February 2021 / Accepted: 26 February 2021 / Published: 9 March 2021
(This article belongs to the Special Issue 2020 Selected Papers from Journal of Imaging Editorial Board Members)
In recent years, automatic tissue phenotyping has attracted increasing interest in the Digital Pathology (DP) field. For Colorectal Cancer (CRC), tissue phenotyping can diagnose the cancer and differentiate between different cancer grades. The development of Whole Slide Images (WSIs) has provided the required data for creating automatic tissue phenotyping systems. In this paper, we study different hand-crafted feature-based and deep learning methods using two popular multi-classes CRC-tissue-type databases: Kather-CRC-2016 and CRC-TP. For the hand-crafted features, we use two texture descriptors (LPQ and BSIF) and their combination. In addition, two classifiers are used (SVM and NN) to classify the texture features into distinct CRC tissue types. For the deep learning methods, we evaluate four Convolutional Neural Network (CNN) architectures (ResNet-101, ResNeXt-50, Inception-v3, and DenseNet-161). Moreover, we propose two Ensemble CNN approaches: Mean-Ensemble-CNN and NN-Ensemble-CNN. The experimental results show that the proposed approaches outperformed the hand-crafted feature-based methods, CNN architectures and the state-of-the-art methods in both databases. View Full-Text
Keywords: digital pathology; colorectal cancer; tissue phenotyping; convolutional neural network; ensemble CNN digital pathology; colorectal cancer; tissue phenotyping; convolutional neural network; ensemble CNN
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MDPI and ACS Style

Paladini, E.; Vantaggiato, E.; Bougourzi, F.; Distante, C.; Hadid, A.; Taleb-Ahmed, A. Two Ensemble-CNN Approaches for Colorectal Cancer Tissue Type Classification. J. Imaging 2021, 7, 51. https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging7030051

AMA Style

Paladini E, Vantaggiato E, Bougourzi F, Distante C, Hadid A, Taleb-Ahmed A. Two Ensemble-CNN Approaches for Colorectal Cancer Tissue Type Classification. Journal of Imaging. 2021; 7(3):51. https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging7030051

Chicago/Turabian Style

Paladini, Emanuela; Vantaggiato, Edoardo; Bougourzi, Fares; Distante, Cosimo; Hadid, Abdenour; Taleb-Ahmed, Abdelmalik. 2021. "Two Ensemble-CNN Approaches for Colorectal Cancer Tissue Type Classification" J. Imaging 7, no. 3: 51. https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging7030051

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