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deep convolutional neural network (dcnn)  (PENTAX Medical Company)

 
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    Structured Review

    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

    Images

    1) Product Images from "Computer-aided detection of colorectal polyps using a newly generated deep convolutional neural network: from development to first clinical experience"

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

    Journal: European Journal of Gastroenterology & Hepatology

    doi: 10.1097/MEG.0000000000002209

    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).
    Figure Legend 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).

    Techniques Used: Diagnostic Assay, Sequencing

    Patient characteristics and withdrawal times
    Figure Legend Snippet: Patient characteristics and withdrawal times

    Techniques Used:

    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)
    Figure Legend 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)

    Techniques Used:

    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
    Figure Legend 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

    Techniques Used:



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    Image Search Results


    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

    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: