ArticleScientific reports2022
A stacking-based artificial intelligence framework for an effective detection and localization of colon polyps.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning driven colorectal polyp analysis: a review of detection, classification and segmentation methods.Frontiers in artificial intelligence · 2026Pooled it
- Harnessing Artificial Intelligence for the Detection and Management of Colorectal Cancer Treatment.Cancer prevention research (Philadelphia, Pa.) · 2024Review
- Development and validation of a cuproptosis-related prognostic model for acute myeloid leukemia patients using machine learning with stacking.Scientific reports · 2024Article
- Automated Diagnosis for Colon Cancer Diseases Using Stacking Transformer Models and Explainable Artificial Intelligence.Diagnostics (Basel, Switzerland) · 2023Article
- Detection of Colorectal Polyps from Colonoscopy Using Machine Learning: A Survey on Modern Techniques.Sensors (Basel, Switzerland) · 2023Review
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Polyp detection through colonoscopy is a widely used method to prevent colorectal cancer. The automation of this process aided by artificial intelligence allows faster and improved detection of polyps that can be missed during a standard colonoscopy. In this work, we propose to implement various object detection algorithms for polyp detection. To improve the mean average precision (mAP) of the detection, we combine the baseline models through a stacking approach. The experiments demonstrate the potential of this new methodology, which can reduce the workload for oncologists and increase the precision of the localization of polyps. Our proposal achieves a mAP of 0.86, translated into an improvement of 34.9% compared to the best baseline model and 28.8% with respect to the weighted boxes fusion ensemble technique.
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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.