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resnet101 (Kaggle Inc)

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Kaggle Inc resnet101
Resnet101, 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/resnet101/resnet101/pm42045534-64-0-18
Average 86 stars, based on 1 article reviews

resnet101 - by Bioz Stars, 2026-10

86/100 stars

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Extraction:

Article Title: Integrating CBAM and Squeeze‐and‐Excitation Networks for Accurate Grapevine Leaf Disease Diagnosis
Article Snippet: .. Patil and More , Feature extraction + Machine Learning , Resnet50 ResNet101+ SVM , Kaggle , 3 diseases +1 healthy , 99.70%. ..

other:

Article Title: Optimizing CNN-Based Diagnosis of Knee Osteoarthritis: Enhancing Model Accuracy with CleanLab Relabeling
Article Snippet: [ ] , ResNet101 , 69 , 5 , 9786 images (Kaggle [ ]) .

Article Title: Deep learning-based citrus plant disease classification using a computationally efficient CNN model.
Article Snippet: ResNet101 was found to be 91.52% accurate when classifying four paddy leaf diseases, which were obtained from both Kaggle and the UCI repository [27].

Article Title: Necessity of Local Modification for Deep Learning Algorithms to Predict Diabetic Retinopathy
Article Snippet: ResNet101 , Kaggle Test , 2.54 , 14.00 , 8.56 , 14.23.

Article Title: Necessity of Local Modification for Deep Learning Algorithms to Predict Diabetic Retinopathy
Article Snippet: ResNet101 , Kaggle Test , 83.89 , 78.12 , 83.89/81.47.

Magnetic Resonance Imaging:

Article Title: Deep Learning Architectures for Brain Tumor Classification
Article Snippet: .. We try different pre-trained CNN models, i.e., VGG19, ResNet101, EfficientNetB0, MobileNetV2, InceptionV3, and DenseNet121, on a Kaggle dataset of 13,196 MRI scans into four categories: pituitary, glioma, meningioma, and no tumor. ..

Image Search Results

Grad-CAM visualization for ResNet101 applied to six ultrasound images (three benign and three malignant). The figure is structured in a 3 × 6 format, where the first column contains benign images, the second column shows the corresponding Grad-CAM results from the original network, and the third column presents the Grad-CAM results from the True network. Similarly, the fourth column contains malignant images, followed by Grad-CAM results from the original network in the fifth column and those from the True network in the sixth column. The color scale from red to blue represents the importance of different image regions, where red indicates the most critical areas used for classification. The selected images include cases where the original network misclassified or had a small probability difference, whereas the True network provided correct classification with a higher probability difference.

Journal: Scientific Reports

Article Title: Effective data selection via deep learning processes and corresponding learning strategies in ultrasound image classification

doi: 10.1038/s41598-025-00416-5

Figure Lengend Snippet: Grad-CAM visualization for ResNet101 applied to six ultrasound images (three benign and three malignant). The figure is structured in a 3 × 6 format, where the first column contains benign images, the second column shows the corresponding Grad-CAM results from the original network, and the third column presents the Grad-CAM results from the True network. Similarly, the fourth column contains malignant images, followed by Grad-CAM results from the original network in the fifth column and those from the True network in the sixth column. The color scale from red to blue represents the importance of different image regions, where red indicates the most critical areas used for classification. The selected images include cases where the original network misclassified or had a small probability difference, whereas the True network provided correct classification with a higher probability difference.

Article Snippet: When training ResNet101 in MATLAB, the input image size is \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:224\times\:224$$\end{document} pixels, with the minibatch size is 100, and the detailed conditions of the Adam optimizer are the gradient decay factor of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:0.9$$\end{document} , the squared gradient decay factor of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:0.999$$\end{document} , and the epsilon of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:{10}^{-8}$$\end{document} .

Techniques:

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