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...
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| Format: | Article |
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MDPI AG
2021-03-01
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| Series: | Journal of Imaging |
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| Online Access: | https://www.mdpi.com/2313-433X/7/3/51 |
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| author | Emanuela Paladini Edoardo Vantaggiato Fares Bougourzi Cosimo Distante Abdenour Hadid Abdelmalik Taleb-Ahmed |
| author_facet | Emanuela Paladini Edoardo Vantaggiato Fares Bougourzi Cosimo Distante Abdenour Hadid Abdelmalik Taleb-Ahmed |
| author_sort | Emanuela Paladini |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T13:26:26Z |
| format | Article |
| id | doaj.art-c0c5a30cd624486385e3eab6b1c3c0e5 |
| institution | Directory Open Access Journal |
| issn | 2313-433X |
| language | English |
| last_indexed | 2024-03-10T13:26:26Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj.art-c0c5a30cd624486385e3eab6b1c3c0e52023-11-21T09:39:55ZengMDPI AGJournal of Imaging2313-433X2021-03-01735110.3390/jimaging7030051Two Ensemble-CNN Approaches for Colorectal Cancer Tissue Type ClassificationEmanuela Paladini0Edoardo Vantaggiato1Fares Bougourzi2Cosimo Distante3Abdenour Hadid4Abdelmalik Taleb-Ahmed5Department of Innovation Engineering, University of Salento, 73100 Lecce, ItalyDepartment of Innovation Engineering, University of Salento, 73100 Lecce, ItalyUniv. Polytechnique Hauts-de-France, Univ. Lille, CNRS, Centrale Lille, UMR 8520—IEMN, F-59313 Valenciennes, FranceDepartment of Innovation Engineering, University of Salento, 73100 Lecce, ItalyUniv. Polytechnique Hauts-de-France, Univ. Lille, CNRS, Centrale Lille, UMR 8520—IEMN, F-59313 Valenciennes, FranceUniv. Polytechnique Hauts-de-France, Univ. Lille, CNRS, Centrale Lille, UMR 8520—IEMN, F-59313 Valenciennes, FranceIn 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.https://www.mdpi.com/2313-433X/7/3/51digital pathologycolorectal cancertissue phenotypingconvolutional neural networkensemble CNN |
| spellingShingle | Emanuela Paladini Edoardo Vantaggiato Fares Bougourzi Cosimo Distante Abdenour Hadid Abdelmalik Taleb-Ahmed Two Ensemble-CNN Approaches for Colorectal Cancer Tissue Type Classification Journal of Imaging digital pathology colorectal cancer tissue phenotyping convolutional neural network ensemble CNN |
| 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 | digital pathology colorectal cancer tissue phenotyping convolutional neural network ensemble CNN |
| url | https://www.mdpi.com/2313-433X/7/3/51 |
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