deep convolutional neural network model (dcnn) Search Results


dcno  (Genovis Inc)
99
Genovis Inc dcno
Dcno, supplied by Genovis Inc, used in various techniques. Bioz Stars score: 99/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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dcno - by Bioz Stars, 2026-03
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deep convolutional neural network (dcnn)  (Optos plc)
90
Optos plc deep convolutional neural network (dcnn)
Deep Convolutional Neural Network (Dcnn), supplied by Optos plc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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deep convolutional neural network (dcnn) - by Bioz Stars, 2026-03
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matlab software  (MathWorks Inc)
90
MathWorks Inc matlab software
Basic structure of <t>DCNN.</t>
Matlab Software, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/matlab software/product/MathWorks Inc
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matlab software - by Bioz Stars, 2026-03
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deep convolutional neural network dcnn  (Deepmind Technologies Ltd)
90
Deepmind Technologies Ltd deep convolutional neural network dcnn
Basic structure of <t>DCNN.</t>
Deep Convolutional Neural Network Dcnn, supplied by Deepmind Technologies Ltd, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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deep convolutional neural network dcnn - by Bioz Stars, 2026-03
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deep convolutional neural networks  (Informa UK Limited)
90
Informa UK Limited deep convolutional neural networks
Basic structure of <t>DCNN.</t>
Deep Convolutional Neural Networks, supplied by Informa UK Limited, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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deep convolutional neural networks - by Bioz Stars, 2026-03
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slactive blt cnn  (Straumann GmbH)
90
Straumann GmbH slactive blt cnn
Basic structure of <t>DCNN.</t>
Slactive Blt Cnn, supplied by Straumann GmbH, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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slactive blt cnn - by Bioz Stars, 2026-03
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deep convolutional neural network (dcnn)  (PENTAX Medical Company)
90
PENTAX Medical Company deep convolutional neural network (dcnn)
Development and diagnostic output of the system. (a) The deep <t>convolutional</t> neural network <t>(DCNN)</t> processes video data as a sequence of single video frames and generates predictions based on the visual evidence of a single video frame. The predictions from individual frames are then fused to provide a more stable detection. (b) Different examples of polyp detection with the DCNN during routine colonoscopy. The computer-aided detection (CAD) system generates the diagnostic output on a second screen on which polyps are highlighted by a bounding box. Note that the DCNN is able to detect multiple polyps in a single frame simultaneously (upper right picture).
Deep Convolutional Neural Network (Dcnn), supplied by PENTAX Medical Company, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/deep convolutional neural network (dcnn)/product/PENTAX Medical Company
Average 90 stars, based on 1 article reviews
deep convolutional neural network (dcnn) - by Bioz Stars, 2026-03
90/100 stars
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deep convolutional neural networks  (KU Leuven)
90
KU Leuven deep convolutional neural networks
Development and diagnostic output of the system. (a) The deep <t>convolutional</t> neural network <t>(DCNN)</t> processes video data as a sequence of single video frames and generates predictions based on the visual evidence of a single video frame. The predictions from individual frames are then fused to provide a more stable detection. (b) Different examples of polyp detection with the DCNN during routine colonoscopy. The computer-aided detection (CAD) system generates the diagnostic output on a second screen on which polyps are highlighted by a bounding box. Note that the DCNN is able to detect multiple polyps in a single frame simultaneously (upper right picture).
Deep Convolutional Neural Networks, supplied by KU Leuven, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/deep convolutional neural networks/product/KU Leuven
Average 90 stars, based on 1 article reviews
deep convolutional neural networks - by Bioz Stars, 2026-03
90/100 stars
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deep convolutional neural network  (Elekta)
90
Elekta deep convolutional neural network
Development and diagnostic output of the system. (a) The deep <t>convolutional</t> neural network <t>(DCNN)</t> processes video data as a sequence of single video frames and generates predictions based on the visual evidence of a single video frame. The predictions from individual frames are then fused to provide a more stable detection. (b) Different examples of polyp detection with the DCNN during routine colonoscopy. The computer-aided detection (CAD) system generates the diagnostic output on a second screen on which polyps are highlighted by a bounding box. Note that the DCNN is able to detect multiple polyps in a single frame simultaneously (upper right picture).
Deep Convolutional Neural Network, supplied by Elekta, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/deep convolutional neural network/product/Elekta
Average 90 stars, based on 1 article reviews
deep convolutional neural network - by Bioz Stars, 2026-03
90/100 stars
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deep convolutional neural networks  (National Institute of Standards and Technology)
90
National Institute of Standards and Technology deep convolutional neural networks
Development and diagnostic output of the system. (a) The deep <t>convolutional</t> neural network <t>(DCNN)</t> processes video data as a sequence of single video frames and generates predictions based on the visual evidence of a single video frame. The predictions from individual frames are then fused to provide a more stable detection. (b) Different examples of polyp detection with the DCNN during routine colonoscopy. The computer-aided detection (CAD) system generates the diagnostic output on a second screen on which polyps are highlighted by a bounding box. Note that the DCNN is able to detect multiple polyps in a single frame simultaneously (upper right picture).
Deep Convolutional Neural Networks, supplied by National Institute of Standards and Technology, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/deep convolutional neural networks/product/National Institute of Standards and Technology
Average 90 stars, based on 1 article reviews
deep convolutional neural networks - by Bioz Stars, 2026-03
90/100 stars
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inception-v2  (Kaggle Inc)
90
Kaggle Inc inception-v2
Development and diagnostic output of the system. (a) The deep <t>convolutional</t> neural network <t>(DCNN)</t> processes video data as a sequence of single video frames and generates predictions based on the visual evidence of a single video frame. The predictions from individual frames are then fused to provide a more stable detection. (b) Different examples of polyp detection with the DCNN during routine colonoscopy. The computer-aided detection (CAD) system generates the diagnostic output on a second screen on which polyps are highlighted by a bounding box. Note that the DCNN is able to detect multiple polyps in a single frame simultaneously (upper right picture).
Inception V2, supplied by Kaggle Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/inception-v2/product/Kaggle Inc
Average 90 stars, based on 1 article reviews
inception-v2 - by Bioz Stars, 2026-03
90/100 stars
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deep convolutional neural network (dcnn)  (Kaggle Inc)
90
Kaggle Inc deep convolutional neural network (dcnn)
Literature Review on ML and DL models for early detection of DR
Deep Convolutional Neural Network (Dcnn), supplied by Kaggle Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/deep convolutional neural network (dcnn)/product/Kaggle Inc
Average 90 stars, based on 1 article reviews
deep convolutional neural network (dcnn) - by Bioz Stars, 2026-03
90/100 stars
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Image Search Results


Basic structure of DCNN.

Journal: PLoS ONE

Article Title: Analysis of the pattern recognition algorithm of broadband satellite modulation signal under deformable convolutional neural networks

doi: 10.1371/journal.pone.0234068

Figure Lengend Snippet: Basic structure of DCNN.

Article Snippet: Then, the broadband satellite modulation signal pattern recognition model based on deformable convolutional neural networks (DCNN) is built, and the broadband satellite signal simulation is conducted based on Matlab software.

Techniques:

Development and diagnostic output of the system. (a) The deep convolutional neural network (DCNN) processes video data as a sequence of single video frames and generates predictions based on the visual evidence of a single video frame. The predictions from individual frames are then fused to provide a more stable detection. (b) Different examples of polyp detection with the DCNN during routine colonoscopy. The computer-aided detection (CAD) system generates the diagnostic output on a second screen on which polyps are highlighted by a bounding box. Note that the DCNN is able to detect multiple polyps in a single frame simultaneously (upper right picture).

Journal: European Journal of Gastroenterology & Hepatology

Article Title: Computer-aided detection of colorectal polyps using a newly generated deep convolutional neural network: from development to first clinical experience

doi: 10.1097/MEG.0000000000002209

Figure Lengend Snippet: Development and diagnostic output of the system. (a) The deep convolutional neural network (DCNN) processes video data as a sequence of single video frames and generates predictions based on the visual evidence of a single video frame. The predictions from individual frames are then fused to provide a more stable detection. (b) Different examples of polyp detection with the DCNN during routine colonoscopy. The computer-aided detection (CAD) system generates the diagnostic output on a second screen on which polyps are highlighted by a bounding box. Note that the DCNN is able to detect multiple polyps in a single frame simultaneously (upper right picture).

Article Snippet: In the current study, we evaluated a novel deep convolutional neural network (DCNN) for automated detection of colorectal polyps that has been developed by a manufacturer of the healthcare industry (Hoya Corporation, Pentax Medical Division, Digital Endoscopy, Friedberg, Germany) in close collaboration with clinical and scientific partners and assessed the performance of the DCNN ex vivo as well as in a first in-human pilot trial.

Techniques: Diagnostic Assay, Sequencing

Patient characteristics and withdrawal times

Journal: European Journal of Gastroenterology & Hepatology

Article Title: Computer-aided detection of colorectal polyps using a newly generated deep convolutional neural network: from development to first clinical experience

doi: 10.1097/MEG.0000000000002209

Figure Lengend Snippet: Patient characteristics and withdrawal times

Article Snippet: In the current study, we evaluated a novel deep convolutional neural network (DCNN) for automated detection of colorectal polyps that has been developed by a manufacturer of the healthcare industry (Hoya Corporation, Pentax Medical Division, Digital Endoscopy, Friedberg, Germany) in close collaboration with clinical and scientific partners and assessed the performance of the DCNN ex vivo as well as in a first in-human pilot trial.

Techniques:

Total number of polyps and adenomas and polyp detection rate and adenoma detection rate after first (without deep convolutional neural network) and second inspection (with deep convolutional neural network)

Journal: European Journal of Gastroenterology & Hepatology

Article Title: Computer-aided detection of colorectal polyps using a newly generated deep convolutional neural network: from development to first clinical experience

doi: 10.1097/MEG.0000000000002209

Figure Lengend Snippet: Total number of polyps and adenomas and polyp detection rate and adenoma detection rate after first (without deep convolutional neural network) and second inspection (with deep convolutional neural network)

Article Snippet: In the current study, we evaluated a novel deep convolutional neural network (DCNN) for automated detection of colorectal polyps that has been developed by a manufacturer of the healthcare industry (Hoya Corporation, Pentax Medical Division, Digital Endoscopy, Friedberg, Germany) in close collaboration with clinical and scientific partners and assessed the performance of the DCNN ex vivo as well as in a first in-human pilot trial.

Techniques:

Characteristics of the polyps detected during first inspection without deep convolutional neural network and those additionally detected during second inspection with deep convolutional neural network

Journal: European Journal of Gastroenterology & Hepatology

Article Title: Computer-aided detection of colorectal polyps using a newly generated deep convolutional neural network: from development to first clinical experience

doi: 10.1097/MEG.0000000000002209

Figure Lengend Snippet: Characteristics of the polyps detected during first inspection without deep convolutional neural network and those additionally detected during second inspection with deep convolutional neural network

Article Snippet: In the current study, we evaluated a novel deep convolutional neural network (DCNN) for automated detection of colorectal polyps that has been developed by a manufacturer of the healthcare industry (Hoya Corporation, Pentax Medical Division, Digital Endoscopy, Friedberg, Germany) in close collaboration with clinical and scientific partners and assessed the performance of the DCNN ex vivo as well as in a first in-human pilot trial.

Techniques:

Literature Review on ML and DL models for early detection of DR

Journal: Multimedia Tools and Applications

Article Title: A critical review on diagnosis of diabetic retinopathy using machine learning and deep learning

doi: 10.1007/s11042-022-12642-4

Figure Lengend Snippet: Literature Review on ML and DL models for early detection of DR

Article Snippet: Xu et al. [ ] have proposed a model which uses label preserving transformation for data augmentation and Deep Convolutional Neural Network (DCNN) based image classification, for the detection of DR, using Kaggle’s dataset.

Techniques: Biomarker Discovery