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Two Ensemble-CNN Approaches for Colorectal Cancer Tissue Type Classification
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 th...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8321410/ https://www.ncbi.nlm.nih.gov/pubmed/34460707 http://dx.doi.org/10.3390/jimaging7030051 |
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author | Paladini, Emanuela Vantaggiato, Edoardo Bougourzi, Fares Distante, Cosimo Hadid, Abdenour Taleb-Ahmed, Abdelmalik |
author_facet | Paladini, Emanuela Vantaggiato, Edoardo Bougourzi, Fares Distante, Cosimo Hadid, Abdenour Taleb-Ahmed, Abdelmalik |
author_sort | Paladini, Emanuela |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8321410 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-83214102021-08-26 Two Ensemble-CNN Approaches for Colorectal Cancer Tissue Type Classification Paladini, Emanuela Vantaggiato, Edoardo Bougourzi, Fares Distante, Cosimo Hadid, Abdenour Taleb-Ahmed, Abdelmalik J Imaging Article 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. MDPI 2021-03-09 /pmc/articles/PMC8321410/ /pubmed/34460707 http://dx.doi.org/10.3390/jimaging7030051 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ). |
spellingShingle | Article Paladini, Emanuela Vantaggiato, Edoardo Bougourzi, Fares Distante, Cosimo Hadid, Abdenour Taleb-Ahmed, Abdelmalik Two Ensemble-CNN Approaches for Colorectal Cancer Tissue Type Classification |
title | Two Ensemble-CNN Approaches for Colorectal Cancer Tissue Type Classification |
title_full | Two Ensemble-CNN Approaches for Colorectal Cancer Tissue Type Classification |
title_fullStr | Two Ensemble-CNN Approaches for Colorectal Cancer Tissue Type Classification |
title_full_unstemmed | Two Ensemble-CNN Approaches for Colorectal Cancer Tissue Type Classification |
title_short | Two Ensemble-CNN Approaches for Colorectal Cancer Tissue Type Classification |
title_sort | two ensemble-cnn approaches for colorectal cancer tissue type classification |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8321410/ https://www.ncbi.nlm.nih.gov/pubmed/34460707 http://dx.doi.org/10.3390/jimaging7030051 |
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