ArticleJournal of imaging2021
Two Ensemble-CNN Approaches for Colorectal Cancer Tissue Type Classification.
Article in Journal of imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 54 citations in OpenAlex.
- Ensemble of Handcrafted and Learned Features for Colorectal Cancer Classification.Journal of imaging informatics in medicine · 2026Article
- Uncovering Hidden Prognostic Patterns in Colorectal Cancer Histology Using Unsupervised Learning: A Computational Pathology Study.Bioengineering (Basel, Switzerland) · 2026Article
- Grad-CAM based deep learning analytics for image-level colon disease classification based on graph neural networks and vision transformers.Frontiers in physiology · 2026Article
- Colorectal cancer detection with enhanced precision using a hybrid supervised and unsupervised learning approach.Scientific reports · 2025Article
- Metallomic Classification of Pulmonary Nodules Using Blood by Deep-Learning-Boosted Synchrotron Radiation X-ray Fluorescence.Environment & health (Washington, D.C.) · 2025Article
- A hybrid framework for enhanced segmentation and classification of colorectal cancer histopathology.Frontiers in artificial intelligence · 2025Article
- [Research progress on colorectal cancer identification based on convolutional neural network].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2024Review
- Color-CADx: a deep learning approach for colorectal cancer classification through triple convolutional neural networks and discrete cosine transform.Scientific reports · 2024Article
- Non-annotated renal histopathological image analysis with deep ensemble learning.Quantitative imaging in medicine and surgery · 2023Article
- SnapEnsemFS: a snapshot ensembling-based deep feature selection model for colorectal cancer histological analysis.Scientific reports · 2023Article
- Histopathological Gastric Cancer Detection on GasHisSDB Dataset Using Deep Ensemble Learning.Diagnostics (Basel, Switzerland) · 2023Article
- Tissue classification and diagnosis of colorectal cancer histopathology images using deep learning algorithms. Is the time ripe for clinical practice implementation?Przeglad gastroenterologiczny · 2023Review
- Development and Evaluation of a Novel Deep-Learning-Based Framework for the Classification of Renal Histopathology Images.Bioengineering (Basel, Switzerland) · 2022Article
- Article
- Deep Learning on Histopathological Images for Colorectal Cancer Diagnosis: A Systematic Review.Diagnostics (Basel, Switzerland) · 2022Review
- Per-COVID-19: A Benchmark Dataset for COVID-19 Percentage Estimation from CT-Scans.Journal of imaging · 2021Article
- Recognition of COVID-19 from CT Scans Using Two-Stage Deep-Learning-Based Approach: CNR-IEMN.Sensors (Basel, Switzerland) · 2021Article
- 3E-Net: Entropy-Based Elastic Ensemble of Deep Convolutional Neural Networks for Grading of Invasive Breast Carcinoma Histopathological Microscopic Images.Entropy (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.