deep convolutional neural network model (dcnn) Search Results


dcno  (Genovis Inc)
93
Genovis Inc dcno
Dcno, supplied by Genovis Inc, used in various techniques. Bioz Stars score: 93/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/dcno/product/Genovis Inc
Average 93 stars, based on 1 article reviews
dcno - by Bioz Stars, 2026-06
93/100 stars
  Buy from Supplier
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
https://www.bioz.com/result/deep convolutional neural network (dcnn)/product/Optos plc
Average 90 stars, based on 1 article reviews
deep convolutional neural network (dcnn) - by Bioz Stars, 2026-06
90/100 stars
  Buy from Supplier
slactive blt cnn  (Straumann GmbH)
90
Straumann GmbH slactive blt cnn
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
https://www.bioz.com/result/slactive blt cnn/product/Straumann GmbH
Average 90 stars, based on 1 article reviews
slactive blt cnn - by Bioz Stars, 2026-06
90/100 stars
  Buy from Supplier
deep convolutional neural networks  (Informa UK Limited)
90
Informa UK Limited deep convolutional neural networks
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
https://www.bioz.com/result/deep convolutional neural networks/product/Informa UK Limited
Average 90 stars, based on 1 article reviews
deep convolutional neural networks - by Bioz Stars, 2026-06
90/100 stars
  Buy from Supplier
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, 2026-06
90/100 stars
  Buy from Supplier
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, 2026-06
90/100 stars
  Buy from Supplier
convolutional layer  (MathWorks Inc)
96
MathWorks Inc convolutional layer
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).
Convolutional Layer, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/convolutional layer/product/MathWorks Inc
Average 96 stars, based on 1 article reviews
convolutional layer - by Bioz Stars, 2026-06
96/100 stars
  Buy from Supplier
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, 2026-06
90/100 stars
  Buy from Supplier
deep convolution neural networks dcnn  (IEEE Access)
90
IEEE Access deep convolution neural networks 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 Convolution Neural Networks Dcnn, supplied by IEEE Access, 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 convolution neural networks dcnn/product/IEEE Access
Average 90 stars, based on 1 article reviews
deep convolution neural networks dcnn - by Bioz Stars, 2026-06
90/100 stars
  Buy from Supplier
dcnn-based offline arabic handwriting recognition  (IEEE Access)
90
IEEE Access dcnn-based offline arabic handwriting recognition
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).
Dcnn Based Offline Arabic Handwriting Recognition, supplied by IEEE Access, 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/dcnn-based offline arabic handwriting recognition/product/IEEE Access
Average 90 stars, based on 1 article reviews
dcnn-based offline arabic handwriting recognition - by Bioz Stars, 2026-06
90/100 stars
  Buy from Supplier
deep convolutional neural network  (Sensornet Ltd)
90
Sensornet Ltd 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 Sensornet Ltd, 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/Sensornet Ltd
Average 90 stars, based on 1 article reviews
deep convolutional neural network - by Bioz Stars, 2026-06
90/100 stars
  Buy from Supplier
dcnn  (EyePACS LLC)
90
EyePACS LLC dcnn
Summary of Deep Learning Methods for DR Classification.
Dcnn, supplied by EyePACS LLC, 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/dcnn/product/EyePACS LLC
Average 90 stars, based on 1 article reviews
dcnn - by Bioz Stars, 2026-06
90/100 stars
  Buy from Supplier

Image Search Results


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:

Summary of Deep Learning Methods for DR Classification.

Journal: Journal of Imaging

Article Title: Retinal Disease Detection Using Deep Learning Techniques: A Comprehensive Review

doi: 10.3390/jimaging9040084

Figure Lengend Snippet: Summary of Deep Learning Methods for DR Classification.

Article Snippet: [ ] , DCNN , EyePACS , 0.757 , , , .

Techniques: