ArticleFrontiers in genetics2022
An Ensemble-Based Deep Convolutional Neural Network for Computer-Aided Polyps Identification From Colonoscopy.
Article in Frontiers in genetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Bridging the gap: Computer-aided detection and Yamada classification system matches expert performance.World journal of gastroenterology · 2025Article
- CADxPolydetect: a clinically explainable hybrid deep learning system for multi-class colorectal lesion detection using augmented colonoscopy images.BMC medical informatics and decision making · 2025Article
- Colorectal cancer unmasked: A synergistic AI framework for Hyper-granular image dissection, precision segmentation, and automated diagnosis.BMC medical imaging · 2025Article
- Multiclassification of Colorectal Polyps from Colonoscopy Images Using AI for Early Diagnosis.Diagnostics (Basel, Switzerland) · 2025Article
- AI-Powered Object Detection in Radiology: Current Models, Challenges, and Future Direction.Journal of imaging · 2025Review
- Colorectal cancer detection with enhanced precision using a hybrid supervised and unsupervised learning approach.Scientific reports · 2025Article
- Exploring vision transformers and XGBoost as deep learning ensembles for transforming carcinoma recognition.Scientific reports · 2024Article
- Efficient colorectal polyp segmentation using wavelet transformation and AdaptUNet: A hybrid U-Net.Heliyon · 2024Article
- Artificial intelligence in colonoscopy: from detection to diagnosis.The Korean journal of internal medicine · 2024Review
- Establishment and validation of an artificial intelligence-based model for real-time detection and classification of colorectal adenoma.Scientific reports · 2024Article
- Deep Learning-Based Classification and Semantic Segmentation of Lung Tuberculosis Lesions in Chest X-ray Images.Diagnostics (Basel, Switzerland) · 2024Article
- Colonoscopy polyp classification via enhanced scattering wavelet Convolutional Neural Network.PloS one · 2024Article
- Artificial intelligence algorithms for real-time detection of colorectal polyps during colonoscopy: a review.American journal of cancer research · 2024Review
- Automated Diagnosis for Colon Cancer Diseases Using Stacking Transformer Models and Explainable Artificial Intelligence.Diagnostics (Basel, Switzerland) · 2023Article
- EnsemDeepCADx: Empowering Colorectal Cancer Diagnosis with Mixed-Dataset Features and Ensemble Fusion CNNs on Evidence-Based CKHK-22 Dataset.Bioengineering (Basel, Switzerland) · 2023Article
- A Real-Time Polyp-Detection System with Clinical Application in Colonoscopy Using Deep Convolutional Neural Networks.Journal of imaging · 2023Article
- Detection of Colorectal Polyps from Colonoscopy Using Machine Learning: A Survey on Modern Techniques.Sensors (Basel, Switzerland) · 2023Review
- A stacking-based artificial intelligence framework for an effective detection and localization of colon polyps.Scientific reports · 2022Article
- Designing optimal convolutional neural network architecture using differential evolution algorithm.Patterns (New York, N.Y.) · 2022Article
- ColoRectalCADx: Expeditious Recognition of Colorectal Cancer with Integrated Convolutional Neural Networks and Visual Explanations Using Mixed Dataset Evidence.Computational and mathematical methods in medicine · 2022Article
Corrections and comments
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Authors and funding
6 authors.
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Abstract
Colorectal cancer (CRC) is the third leading cause of cancer death globally. Early detection and removal of precancerous polyps can significantly reduce the chance of CRC patient death. Currently, the polyp detection rate mainly depends on the skill and expertise of gastroenterologists. Over time, unidentified polyps can develop into cancer. Machine learning has recently emerged as a powerful method in assisting clinical diagnosis. Several classification models have been proposed to identify polyps, but their performance has not been comparable to an expert endoscopist yet. Here, we propose a multiple classifier consultation strategy to create an effective and powerful classifier for polyp identification. This strategy benefits from recent findings that different classification models can better learn and extract various information within the image. Therefore, our Ensemble classifier can derive a more consequential decision than each individual classifier. The extracted combined information inherits the ResNet's advantage of residual connection, while it also extracts objects when covered by occlusions through depth-wise separable convolution layer of the Xception model. Here, we applied our strategy to still frames extracted from a colonoscopy video. It outperformed other state-of-the-art techniques with a performance measure greater than 95% in each of the algorithm parameters. Our method will help researchers and gastroenterologists develop clinically applicable, computational-guided tools for colonoscopy screening. It may be extended to other clinical diagnoses that rely on image.
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