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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Bibliographic Details
Published in:Journal of imaging 2021-03, Vol.7 (3), p.51
Main Authors: Paladini, Emanuela, Vantaggiato, Edoardo, Bougourzi, Fares, Distante, Cosimo, Hadid, Abdenour, Taleb-Ahmed, Abdelmalik
Format: Article
Language:English
Subjects:
Artificial neural networks
Cancer
Classification
Colorectal cancer
Computer Science
convolutional neural network
Deep learning
digital pathology
Digitization
ensemble CNN
Human health and pathology
Hépatology and Gastroenterology
Image Processing
Life Sciences
Machine learning
Neural and Evolutionary Computing
Pathology
Performance evaluation
Texture
tissue phenotyping
Tissues and Organs
Tumors
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Summary: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.
ISSN:2313-433X
2313-433X
DOI:10.3390/jimaging7030051