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pretrained vgg19 network (MathWorks Inc)

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MathWorks Inc pretrained vgg19 network
Pretrained Vgg19 Network, 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 vgg19 network/product/MathWorks Inc
Average 90 stars, based on 1 article reviews

pretrained vgg19 network - by Bioz Stars, 2026-05

90/100 stars

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pretrained vgg19 network (MathWorks Inc)

pretrained vgg19 network - by Bioz Stars, 2026-05

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pretrained vgg19 (MathWorks Inc)

MathWorks Inc pretrained vgg19

Deep learning (DL)–based feature extraction scheme using <t>VGG19.</t> VGG19 contains 16 convolutional layers, 5 max-pooling layers, and 3 fully connected layers. The average-pooling layers were used for extracting DL-based features. Feature maps and feature vectors after every layer are shown as cuboids and rectangles, respectively. The feature map depth and feature number are shown. A concatenation of fluid-attenuated inversion recovery (FLAIR), T2-weighted (T2w), and contrast-enhanced T1-weighted (CE-T1w) regions of interest (ROIs) was input into the pretrained VGG19 for feature extraction. By average-pooling along the spatial dimensions, 1472 DL-based features were extracted from max-pooling feature maps. Abbreviations: Conv = convolutional layer; ReLU = rectified linear unit.

Pretrained Vgg19, 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/pretrained vgg19/product/MathWorks Inc
Average 96 stars, based on 1 article reviews

pretrained vgg19 - by Bioz Stars, 2026-05

96/100 stars

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Deep learning (DL)–based feature extraction scheme using VGG19. VGG19 contains 16 convolutional layers, 5 max-pooling layers, and 3 fully connected layers. The average-pooling layers were used for extracting DL-based features. Feature maps and feature vectors after every layer are shown as cuboids and rectangles, respectively. The feature map depth and feature number are shown. A concatenation of fluid-attenuated inversion recovery (FLAIR), T2-weighted (T2w), and contrast-enhanced T1-weighted (CE-T1w) regions of interest (ROIs) was input into the pretrained VGG19 for feature extraction. By average-pooling along the spatial dimensions, 1472 DL-based features were extracted from max-pooling feature maps. Abbreviations: Conv = convolutional layer; ReLU = rectified linear unit.

Journal: Advances in Radiation Oncology

Article Title: An Automatic Deep Learning–Based Workflow for Glioblastoma Survival Prediction Using Preoperative Multimodal MR Images: A Feasibility Study

doi: 10.1016/j.adro.2021.100746

Figure Lengend Snippet: Deep learning (DL)–based feature extraction scheme using VGG19. VGG19 contains 16 convolutional layers, 5 max-pooling layers, and 3 fully connected layers. The average-pooling layers were used for extracting DL-based features. Feature maps and feature vectors after every layer are shown as cuboids and rectangles, respectively. The feature map depth and feature number are shown. A concatenation of fluid-attenuated inversion recovery (FLAIR), T2-weighted (T2w), and contrast-enhanced T1-weighted (CE-T1w) regions of interest (ROIs) was input into the pretrained VGG19 for feature extraction. By average-pooling along the spatial dimensions, 1472 DL-based features were extracted from max-pooling feature maps. Abbreviations: Conv = convolutional layer; ReLU = rectified linear unit.

Article Snippet: We used a pretrained VGG19 that is available in the deep learning toolbox (version 12.0) from MATLAB (version 9.5, R2018b).

Techniques: Extraction