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Kaggle Inc vgg-19
Vgg 19, supplied by Kaggle 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/product/vgg-19/vgg+19/pm40431972-429-23-26
Average 90 stars, based on 1 article reviews
vgg-19 - by Bioz Stars, 2026-09
90/100 stars

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Related Articles

Comparison:

Article Title: Brain Tumor Classification based on Improved Stacked Ensemble Deep Learning Methods
Article Snippet: The Kaggle data set was used to build the architecture of the pre-trained models VGG-19, Inception V3, and Resnet 101.

Article Title: Detection of Diabetic Retinopathy Using VGG19 and ResNet 50 Models
Article Snippet: In the works of Sudha and Gareshbadu (2020), VGG-19 was trained and tested with images from the Kaggle dataset to achieve a sensitivity of 82% for classifying DR into different stages.

Article Title: A Systematic Review of AI-Based Techniques for Automated Waste Classification
Article Snippet: Evaluation of VGG-19 performance: Although VGG-16 generally outperforms VGG-19, studies show that deeper architectures can be beneficial under specific conditions, like [ ] utilized VGG-19 on the Kaggle garbage classification dataset, achieving 91% accuracy, whereas Ref. [ ] reported only 56% accuracy using the same model, indicating that VGG-19 requires careful hyperparameter tuning and large-scale datasets to perform optimally.

Article Title: The role of artificial intelligence in the diagnosis of diabetic retinopathy through retinal lesion features: a narrative review
Article Snippet: 2019 , Mateen et al. ( ) , VGG-19, SVD, and PCA , Kaggle , Accuracy: 98.34%.

Article Title: The role of artificial intelligence in the diagnosis of diabetic retinopathy through retinal lesion features: a narrative review
Article Snippet: 2021 , Sudha and Ganeshbabu ( ) , Deep neural network, VGG-19, gradient descent, structure tensor , Kaggle , Sensitivity: 82%; accuracy: 96%.

Article Title: A Systematic Review of AI-Based Techniques for Automated Waste Classification.
Article Snippet: Evaluation of VGG-19 performance: Although VGG-16 generally outperforms VGG-19, studies show that deeper architectures can be beneficial under specific conditions, like [48] utilized VGG-19 on the Kaggle garbage classification dataset, achieving 91% accuracy, whereas Ref. [45] reported only 56% accuracy using the same model, indicating that VGG19 requires careful hyperparameter tuning and large-scale datasets to perform optimally.

Article Title: A Deep Learning Based Intelligent Decision Support System for Automatic Detection of Brain Tumor
Article Snippet: Proposed , Kaggle , VGG-16, VGG-19 , 99.24 , 99 , 99 , 99 , 99.




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