ArticleSensors (Basel, Switzerland)2021
Automatic Detection of Colorectal Polyps Using Transfer Learning.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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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
11 citing papers in PubMed, 34 citations in OpenAlex.
- Integrating histopathology and immune marker analysis for machine learning-based colorectal cancer prognostics.Diagnostic pathology · 2026Article
- Advanced AI-Powered System for Comprehensive Thyroid Cancer Detection and Malignancy Risk Assessment.Life (Basel, Switzerland) · 2025Article
- Disease detection on exterior surfaces of buildings using deep learning in China.Scientific reports · 2025Article
- Deep Learning in Endoscopic Ultrasound: A Breakthrough in Detecting Distal Cholangiocarcinoma.Cancers · 2024Article
- Computer-Assisted Algorithm for Quantification of Fibrosis by Native Cardiac CT: A Pilot Study.Journal of clinical medicine · 2024Article
- Artificial Intelligence in the Diagnosis of Colorectal Cancer: A Literature Review.Diagnostics (Basel, Switzerland) · 2024Review
- A Multiscale Polyp Detection Approach for GI Tract Images Based on Improved DenseNet and Single-Shot Multibox Detector.Diagnostics (Basel, Switzerland) · 2023Article
- A Review Paper about Deep Learning for Medical Image Analysis.Computational and mathematical methods in medicine · 2023Review
- Multi-Scale Hybrid Network for Polyp Detection in Wireless Capsule Endoscopy and Colonoscopy Images.Diagnostics (Basel, Switzerland) · 2022Article
- Automatic Detection and Segmentation of Colorectal Cancer with Deep Residual Convolutional Neural Network.Evidence-based complementary and alternative medicine : eCAM · 2022Article
- Modelling in Synthesis and Optimization of Active Vaccinal Components.Nanomaterials (Basel, Switzerland) · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
Colorectal cancer is the second leading cause of cancer death and ranks third worldwide in diagnosed malignant pathologies (1.36 million new cases annually). An increase in the diversity of treatment options as well as a rising population require novel diagnostic tools. Current diagnostics involve critical human thinking, but the decisional process loses accuracy due to the increased number of modulatory factors involved. The proposed computer-aided diagnosis system analyses each colonoscopy and provides predictions that will help the clinician to make the right decisions. Artificial intelligence is included in the system both offline and online image processing tools. Aiming to improve the diagnostic process of colon cancer patients, an application was built that allows the easiest and most intuitive interaction between medical staff and the proposed diagnosis system. The developed tool uses two networks. The first, a convolutional neural network, is capable of classifying eight classes of tissue with a sensitivity of 98.13% and an F1 score of 98.14%, while the second network, based on semantic segmentation, can identify the malignant areas with a Jaccard index of 75.18%. The results could have a direct impact on personalised medicine combining clinical knowledge with the computing power of intelligent algorithms.
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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.