ArticleJournal of pathology informatics2017
Classifications of Multispectral Colorectal Cancer Tissues Using Convolution Neural Network.
Article in Journal of pathology informatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed.
- SurvGRN: a multi-feature fusion framework for bladder cancer survival prediction.International journal of surgery (London, England) · 2026Article
- Deep-learning-based artificial intelligence approaches for grading and progression prediction of clear cell renal cell carcinoma.iScience · 2026Article
- Two-Step Ensemble Convolutional Neural Networks for Colonoscopic Biopsy Classification Resembling Pathologists' Process.Journal of Korean medical science · 2026Article
- Deep Learning for Pathology: YOLOv8 with EigenCAM for Reliable Colorectal Cancer Diagnostics.Bioengineering (Basel, Switzerland) · 2025Article
- Research status and trends of deep learning in colorectal cancer (2011-2023): Bibliometric analysis and visualization.World journal of gastrointestinal oncology · 2025Article
- Integrating artificial intelligence with smartphone-based imaging for cancer detection in vivo.Biosensors & bioelectronics · 2025Review
- The development of an efficient artificial intelligence-based classification approach for colorectal cancer response to radiochemotherapy: deep learning vs. machine learning.Scientific reports · 2025Article
- Lung and Colon Cancer Detection Using a Deep AI Model.Cancers · 2024Article
- Article
- Enhancing colorectal cancer histology diagnosis using modified deep neural networks optimizer.Scientific reports · 2024Article
- Ensemble-based multi-tissue classification approach of colorectal cancer histology images using a novel hybrid deep learning framework.Scientific reports · 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
- XDecompo: Explainable Decomposition Approach in Convolutional Neural Networks for Tumour Image Classification.Sensors (Basel, Switzerland) · 2022Article
- Deep Learning on Histopathological Images for Colorectal Cancer Diagnosis: A Systematic Review.Diagnostics (Basel, Switzerland) · 2022Review
- Applications of Artificial Intelligence in Screening, Diagnosis, Treatment, and Prognosis of Colorectal Cancer.Current oncology (Toronto, Ont.) · 2022Review
- Convolutional Neural Network-Based Automatic Classification of Colorectal and Prostate Tumor Biopsies Using Multispectral Imagery: System Development Study.JMIR bioinformatics and biotechnology · 2022Article
- Preoperative AI-Driven Fluorescence Diagnosis of Non-Melanoma Skin Cancer.Diagnostics (Basel, Switzerland) · 2021Article
- Accurate recognition of colorectal cancer with semi-supervised deep learning on pathological images.Nature communications · 2021Article
- Article
- Role of artificial intelligence in multidisciplinary imaging diagnosis of gastrointestinal diseases.World journal of gastroenterology · 2021Review
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundColorectal cancer (CRC) is the third most common cancer among men and women. Its diagnosis in early stages, typically done through the analysis of colon biopsy images, can greatly improve the chances of a successful treatment. This paper proposes to use convolution neural networks (CNNs) to predict three tissue types related to the progression of CRC: benign hyperplasia (BH), intraepithelial neoplasia (IN), and carcinoma (Ca).
methodsMultispectral biopsy images of thirty CRC patients were retrospectively analyzed. Images of tissue samples were divided into three groups, based on their type (10 BH, 10 IN, and 10 Ca). An active contour model was used to segment image regions containing pathological tissues. Tissue samples were classified using a CNN containing convolution, max-pooling, and fully-connected layers. Available tissue samples were split into a training set, for learning the CNN parameters, and test set, for evaluating its performance.
resultsAn accuracy of 99.17% was obtained from segmented image regions, outperforming existing approaches based on traditional feature extraction, and classification techniques.
conclusionsExperimental results demonstrate the effectiveness of CNN for the classification of CRC tissue types, in particular when using presegmented regions of interest.
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