ReviewSensors (Basel, Switzerland)2022
Colon Cancer Diagnosis Based on Machine Learning and Deep Learning: Modalities and Analysis Techniques.
Review in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses that pooled it.
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
20 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Deep learning driven colorectal polyp analysis: a review of detection, classification and segmentation methods.Frontiers in artificial intelligence · 2026Pooled it
- Advancements in Image-Based Analyses for Morphology and Staging of Colon Cancer: A Comprehensive Review.BioMed research international · 2025Pooled it
- Machine learning integration of tissue-specific metagenomic signatures for colorectal cancer diagnosis.Journal of applied genetics · 2026Article
- Automatic Recognition and Prognostic Prediction of Colorectal Liver Metastases Using a Multi-Scale Deep Learning Framework: Model Development and Validation Study.JMIR medical informatics · 2026Article
- Review
- Multicenter machine learning study for long-term prediction of acute kidney injury after complete mesocolic excision: integrating inflammatory biomarkers and transfusion-related risk factors.Frontiers in oncology · 2026Article
- Hybrid deep learning with attention fusion for enhanced colon cancer detection.Scientific reports · 2025Article
- Prediction of longitudinal outcomes and novel cluster identification in epilepsy.Scientific reports · 2025Article
- Weakly Supervised Gland Segmentation Based on Hierarchical Attention Fusion and Pixel Affinity Learning.Bioengineering (Basel, Switzerland) · 2025Article
- Progress in the application research of cervical cancer screening developed by artificial intelligence in large populations.Discover oncology · 2025Review
- Cervical cancer prediction using machine learning models based on routine blood analysis.Scientific reports · 2025Article
- Article
- ARTIFICIAL INTELLIGENCE IN THE HISTOLOGICAL DIAGNOSIS OF COLON CANCER: A RAY OF HOPE.Arquivos de gastroenterologia · 2025Article
- Efficient colorectal polyp segmentation using wavelet transformation and AdaptUNet: A hybrid U-Net.Heliyon · 2024Article
- Colon Cancer Disease Diagnosis Based on Convolutional Neural Network and Fishier Mantis Optimizer.Diagnostics (Basel, Switzerland) · 2024Article
- Artificial intelligence: clinical applications and future advancement in gastrointestinal cancers.Frontiers in artificial intelligence · 2024Review
- Advancements and Challenges in the Image-Based Diagnosis of Lung and Colon Cancer: A Comprehensive Review.Cancer informatics · 2024Review
- Using DUCK-Net for polyp image segmentation.Scientific reports · 2023Article
- Identification of fibroblast-related genes based on single-cell and machine learning to predict the prognosis and endocrine metabolism of pancreatic cancer.Frontiers in endocrinology · 2023Article
- The role of deep learning in diagnosing colorectal cancer.Przeglad gastroenterologiczny · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
The treatment and diagnosis of colon cancer are considered to be social and economic challenges due to the high mortality rates. Every year, around the world, almost half a million people contract cancer, including colon cancer. Determining the grade of colon cancer mainly depends on analyzing the gland's structure by tissue region, which has led to the existence of various tests for screening that can be utilized to investigate polyp images and colorectal cancer. This article presents a comprehensive survey on the diagnosis of colon cancer. This covers many aspects related to colon cancer, such as its symptoms and grades as well as the available imaging modalities (particularly, histopathology images used for analysis) in addition to common diagnosis systems. Furthermore, the most widely used datasets and performance evaluation metrics are discussed. We provide a comprehensive review of the current studies on colon cancer, classified into deep-learning (DL) and machine-learning (ML) techniques, and we identify their main strengths and limitations. These techniques provide extensive support for identifying the early stages of cancer that lead to early treatment of the disease and produce a lower mortality rate compared with the rate produced after symptoms develop. In addition, these methods can help to prevent colorectal cancer from progressing through the removal of pre-malignant polyps, which can be achieved using screening tests to make the disease easier to diagnose. Finally, the existing challenges and future research directions that open the way for future work in this field are presented.
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.