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pretrained resnet50 architecture  (MathWorks Inc)


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    Structured Review

    MathWorks Inc pretrained resnet50 architecture
    A schematic of deep learning (DL) model training, validation, and performance assessment for prognosis of central serous chorioretinopathy (CSC) using optical coherence tomography (OCT) image sets. Baseline horizontal OCT B-scan crossing the fovea and en face images of retinal thickness, mid-retinal, ellipsoid zone (EZ), and choroidal layers were saved as a JPEG (.jpg) file from each patient and labeled as acute or persistent disease status at 6 months from baseline. The collected images underwent resizing and preprocessing of crop and contrast adjustment. Then, a deep learning model using <t>ResNet50</t> architecture was trained and validated to predict the impact on the training and validation sets. Next, a performance evaluation on the test set was carried out. Visual explanation analysis was performed using a heatmap generated with gradient-weighted class activation mapping (Grad-CAM). Image sets with high accuracy were concatenated for bimodal imaging model training.
    Pretrained Resnet50 Architecture, 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/pretrained resnet50 architecture/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    pretrained resnet50 architecture - by Bioz Stars, 2026-05
    90/100 stars

    Images

    1) Product Images from "Predicting persistent central serous chorioretinopathy using multiple optical coherence tomographic images by deep learning"

    Article Title: Predicting persistent central serous chorioretinopathy using multiple optical coherence tomographic images by deep learning

    Journal: Scientific Reports

    doi: 10.1038/s41598-022-13473-x

    A schematic of deep learning (DL) model training, validation, and performance assessment for prognosis of central serous chorioretinopathy (CSC) using optical coherence tomography (OCT) image sets. Baseline horizontal OCT B-scan crossing the fovea and en face images of retinal thickness, mid-retinal, ellipsoid zone (EZ), and choroidal layers were saved as a JPEG (.jpg) file from each patient and labeled as acute or persistent disease status at 6 months from baseline. The collected images underwent resizing and preprocessing of crop and contrast adjustment. Then, a deep learning model using ResNet50 architecture was trained and validated to predict the impact on the training and validation sets. Next, a performance evaluation on the test set was carried out. Visual explanation analysis was performed using a heatmap generated with gradient-weighted class activation mapping (Grad-CAM). Image sets with high accuracy were concatenated for bimodal imaging model training.
    Figure Legend Snippet: A schematic of deep learning (DL) model training, validation, and performance assessment for prognosis of central serous chorioretinopathy (CSC) using optical coherence tomography (OCT) image sets. Baseline horizontal OCT B-scan crossing the fovea and en face images of retinal thickness, mid-retinal, ellipsoid zone (EZ), and choroidal layers were saved as a JPEG (.jpg) file from each patient and labeled as acute or persistent disease status at 6 months from baseline. The collected images underwent resizing and preprocessing of crop and contrast adjustment. Then, a deep learning model using ResNet50 architecture was trained and validated to predict the impact on the training and validation sets. Next, a performance evaluation on the test set was carried out. Visual explanation analysis was performed using a heatmap generated with gradient-weighted class activation mapping (Grad-CAM). Image sets with high accuracy were concatenated for bimodal imaging model training.

    Techniques Used: Biomarker Discovery, Tomography, Labeling, Generated, Activation Assay, Imaging



    Similar Products

    pretrained resnet50 architecture  (MathWorks Inc)
    90
    MathWorks Inc pretrained resnet50 architecture
    A schematic of deep learning (DL) model training, validation, and performance assessment for prognosis of central serous chorioretinopathy (CSC) using optical coherence tomography (OCT) image sets. Baseline horizontal OCT B-scan crossing the fovea and en face images of retinal thickness, mid-retinal, ellipsoid zone (EZ), and choroidal layers were saved as a JPEG (.jpg) file from each patient and labeled as acute or persistent disease status at 6 months from baseline. The collected images underwent resizing and preprocessing of crop and contrast adjustment. Then, a deep learning model using <t>ResNet50</t> architecture was trained and validated to predict the impact on the training and validation sets. Next, a performance evaluation on the test set was carried out. Visual explanation analysis was performed using a heatmap generated with gradient-weighted class activation mapping (Grad-CAM). Image sets with high accuracy were concatenated for bimodal imaging model training.
    Pretrained Resnet50 Architecture, 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/pretrained resnet50 architecture/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    pretrained resnet50 architecture - by Bioz Stars, 2026-05
    90/100 stars
    		  Buy from Supplier

    Image Search Results


    A schematic of deep learning (DL) model training, validation, and performance assessment for prognosis of central serous chorioretinopathy (CSC) using optical coherence tomography (OCT) image sets. Baseline horizontal OCT B-scan crossing the fovea and en face images of retinal thickness, mid-retinal, ellipsoid zone (EZ), and choroidal layers were saved as a JPEG (.jpg) file from each patient and labeled as acute or persistent disease status at 6 months from baseline. The collected images underwent resizing and preprocessing of crop and contrast adjustment. Then, a deep learning model using ResNet50 architecture was trained and validated to predict the impact on the training and validation sets. Next, a performance evaluation on the test set was carried out. Visual explanation analysis was performed using a heatmap generated with gradient-weighted class activation mapping (Grad-CAM). Image sets with high accuracy were concatenated for bimodal imaging model training.

    Journal: Scientific Reports

    Article Title: Predicting persistent central serous chorioretinopathy using multiple optical coherence tomographic images by deep learning

    doi: 10.1038/s41598-022-13473-x

    Figure Lengend Snippet: A schematic of deep learning (DL) model training, validation, and performance assessment for prognosis of central serous chorioretinopathy (CSC) using optical coherence tomography (OCT) image sets. Baseline horizontal OCT B-scan crossing the fovea and en face images of retinal thickness, mid-retinal, ellipsoid zone (EZ), and choroidal layers were saved as a JPEG (.jpg) file from each patient and labeled as acute or persistent disease status at 6 months from baseline. The collected images underwent resizing and preprocessing of crop and contrast adjustment. Then, a deep learning model using ResNet50 architecture was trained and validated to predict the impact on the training and validation sets. Next, a performance evaluation on the test set was carried out. Visual explanation analysis was performed using a heatmap generated with gradient-weighted class activation mapping (Grad-CAM). Image sets with high accuracy were concatenated for bimodal imaging model training.

    Article Snippet: The models were trained using pretrained ResNet50 architecture on MATLAB 2021b (MathWorks, Inc., Natick, MA, USA) for each image set (B-scan, retinal thickness, mid-retinal, EZ, and choroid).

    Techniques: Biomarker Discovery, Tomography, Labeling, Generated, Activation Assay, Imaging