ArticleScientific reports2023
Ensemble-based multi-tissue classification approach of colorectal cancer histology images using a novel hybrid deep learning framework.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
21 citing papers in PubMed, 57 citations in OpenAlex.
- CSNet: Cognitive Simulation Network for Reliable Histopathology-Based Cancer Diagnosis.Journal of imaging informatics in medicine · 2026Article
- Deep Learning Approach for Biomedical Image Classification.Journal of imaging informatics in medicine · 2026Review
- Compact deep learning models for colon histopathology focusing performance and generalization challenges.Scientific reports · 2026Article
- Multimodal-multiscale hybrid fusion network for automated esophageal cancer diagnosis.iScience · 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
- Ensemble CNN for colon cancer detection using histopathological image.Frontiers in oncology · 2026Article
- MobileDANet integrating transfer learning and dynamic attention for classifying multi target histopathology images with explainable AI.Scientific reports · 2025Article
- Colorectal cancer unmasked: A synergistic AI framework for Hyper-granular image dissection, precision segmentation, and automated diagnosis.BMC medical imaging · 2025Article
- Photoacoustic-Integrated Multimodal Approach for Colorectal Cancer Diagnosis.ACS biomaterials science & engineering · 2025Review
- Accurate colorectal cancer detection using a random hinge exponential distribution coupled attention network on pathological images.Abdominal radiology (New York) · 2025Article
- Leveraging Radiomics and Hybrid Quantum-Classical Convolutional Networks for Non-Invasive Detection of Microsatellite Instability in Colorectal Cancer.Molecular imaging and biology · 2025Article
- Advanced deep learning for multi-class colorectal cancer histopathology: integrating transfer learning and ensemble methods.Quantitative imaging in medicine and surgery · 2025Article
- Colorectal cancer detection with enhanced precision using a hybrid supervised and unsupervised learning approach.Scientific reports · 2025Article
- A lightweight CNN for colon cancer tissue classification and visualization.Frontiers in oncology · 2025Article
- Deep learning-based classification of colorectal cancer in histopathology images for category detection.Biology methods & protocols · 2025Article
- A depth analysis of recent innovations in non-invasive techniques using artificial intelligence approach for cancer prediction.Medical & biological engineering & computing · 2024Review
- An efficient colorectal cancer detection network using atrous convolution with coordinate attention transformer and histopathological images.Scientific reports · 2024Article
- Color-CADx: a deep learning approach for colorectal cancer classification through triple convolutional neural networks and discrete cosine transform.Scientific reports · 2024Article
- Novel research and future prospects of artificial intelligence in cancer diagnosis and treatment.Journal of hematology & oncology · 2023Review
- An improved multi-scale gradient generative adversarial network for enhancing classification of colorectal cancer histological images.Frontiers in oncology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Colorectal cancer (CRC) is the second leading cause of cancer death in the world, so digital pathology is essential for assessing prognosis. Due to the increasing resolution and quantity of whole slide images (WSIs), as well as the lack of annotated information, previous methodologies cannot be generalized as effective decision-making systems. Since deep learning (DL) methods can handle large-scale applications, they can provide a viable alternative to histopathology image (HI) analysis. DL architectures, however, may not be sufficient to classify CRC tissues based on anatomical histopathology data. A dilated ResNet (dResNet) structure and attention module are used to generate deep feature maps in order to classify multiple tissues in HIs. In addition, neighborhood component analysis (NCA) overcomes the constraint of computational complexity. Data is fed into a deep support vector machine (SVM) based on an ensemble learning algorithm called DeepSVM after the features have been selected. CRC-5000 and NCT-CRC-HE-100 K datasets were analyzed to validate and test the hybrid procedure. We demonstrate that the hybrid model achieves 98.75% and 99.76% accuracy on CRC datasets. The results showed that only pathologists' labels could successfully classify unseen WSIs. Furthermore, the hybrid deep learning method outperforms state-of-the-art approaches in terms of computational efficiency and time. Using the proposed mechanism for tissue analysis, it will be possible to correctly predict CRC based on accurate pathology image classification.
Indexed as
Identifiers
What OpenQuestion holds
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.