ArticleHealthcare (Basel, Switzerland)2022
Rapid Polyp Classification in Colonoscopy Using Textural and Convolutional Features.
Article in Healthcare (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- A roadmap of artificial intelligence applications in pediatric surgery: a comprehensive review of applications, challenges, and ethical considerations.Pediatric surgery international · 2025Review
- Polyp-Size: A Precise Endoscopic Dataset for AI-Driven Polyp Sizing.Scientific data · 2025Article
- Detecting microsatellite instability in colorectal cancer using Transformer-based colonoscopy image classification and retrieval.PloS one · 2024Article
- Identification of LncPVT1 and CircPVT1 as prognostic biomarkers in human colorectal polyps.Scientific reports · 2023Article
- Automated classification of polyps using deep learning architectures and few-shot learning.BMC medical imaging · 2023Article
- Detection of Colorectal Polyps from Colonoscopy Using Machine Learning: A Survey on Modern Techniques.Sensors (Basel, Switzerland) · 2023Review
- Analyzing drama metadata through machine learning to gain insights into social information dissemination patterns.PloS one · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
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Abstract
Colorectal cancer is the leading cause of cancer-associated morbidity and mortality worldwide. One of the causes of developing colorectal cancer is untreated colon adenomatous polyps. Clinically, polyps are detected in colonoscopy and the malignancies are determined according to the biopsy. To provide a quick and objective assessment to gastroenterologists, this study proposed a quantitative polyp classification via various image features in colonoscopy. The collected image database was composed of 1991 images including 1053 hyperplastic polyps and 938 adenomatous polyps and adenocarcinomas. From each image, textural features were extracted and combined in machine learning classifiers and machine-generated features were automatically selected in deep convolutional neural networks (DCNN). The DCNNs included AlexNet, Inception-V3, ResNet-101, and DenseNet-201. AlexNet trained from scratch achieved the best performance of 96.4% accuracy which is better than transfer learning and textural features. Using the prediction models, the malignancy level of polyps can be evaluated during a colonoscopy to provide a rapid treatment plan.
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Registered trials
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