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resnet 50 pretrained cnn  (Kaggle Inc)

 
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    Kaggle Inc resnet 50 pretrained cnn
    Resnet 50 Pretrained Cnn, supplied by Kaggle Inc, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/pretrained+cnn/10__48084_slash_etasr__11455-89-15-14?v=Kaggle+Inc
    Average 86 stars, based on 1 article reviews
    resnet 50 pretrained cnn - by Bioz Stars, 2026-07
    86/100 stars

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    Image Search Results


    AF classification performance on train, validation, and test datasets using ResNet101 models

    Journal: BMC Medical Informatics and Decision Making

    Article Title: Deep learning for the classification of atrial fibrillation using wavelet transform-based visual images

    doi: 10.1186/s12911-025-02872-5

    Figure Lengend Snippet: AF classification performance on train, validation, and test datasets using ResNet101 models

    Article Snippet: The pretrained CNN: ResNet101, selected from Matlab's 2022a Pretrained CNN document [ ], was employed for our research.

    Techniques: Biomarker Discovery

    Dual deep learning computer vision algorithms deployed for automated trap identification and cytotoxicity analysis. (a) Automated computer vision analysis was divided into four stages: (1) the generation of a microfluidic structure ground truth data set and training of the first regions with convolutional neural network (RCNN‐1) to identify microfluidic cell trapping structures, (2) the deployment of RCNN‐1 to identify and enumerate relevant cell traps in the actual experimental data set, (3) the generation of a cell type (i.e., tumor cell, NK cell) and status (i.e., dead, live) ground truth data set and training of RCNN‐2 to identify cell death events indicated with caspase 3/7 reporter dye, and (4) deployment of RCNN‐2 to track and quantify cell death events in experimental data sets. (b) A flowchart detailing the specific steps taken at each stage in the computer vision analysis process.

    Journal: Bioengineering & Translational Medicine

    Article Title: An AI ‐assisted integrated, scalable, single‐cell phenomic‐transcriptomic platform to elucidate intratumor heterogeneity against immune response

    doi: 10.1002/btm2.10628

    Figure Lengend Snippet: Dual deep learning computer vision algorithms deployed for automated trap identification and cytotoxicity analysis. (a) Automated computer vision analysis was divided into four stages: (1) the generation of a microfluidic structure ground truth data set and training of the first regions with convolutional neural network (RCNN‐1) to identify microfluidic cell trapping structures, (2) the deployment of RCNN‐1 to identify and enumerate relevant cell traps in the actual experimental data set, (3) the generation of a cell type (i.e., tumor cell, NK cell) and status (i.e., dead, live) ground truth data set and training of RCNN‐2 to identify cell death events indicated with caspase 3/7 reporter dye, and (4) deployment of RCNN‐2 to track and quantify cell death events in experimental data sets. (b) A flowchart detailing the specific steps taken at each stage in the computer vision analysis process.

    Article Snippet: RCNN‐1 was a pretrained CNN from MATLAB with layer architecture based off Resnet‐50.

    Techniques: