kaggle alexnet (Kaggle Inc)
86
Structured Review
Kaggle Inc
kaggle alexnet
Kaggle Alexnet, 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/kaggle+alexnet/alexnet/pm41068276-221-183-183
Average 86 stars, based on 1 article reviews
Kaggle Alexnet, 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/kaggle+alexnet/alexnet/pm41068276-221-183-183
Average 86 stars, based on 1 article reviews
kaggle alexnet - by Bioz Stars,
2026-10
86/100 stars
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Related Articles
Imaging:Article Title: IoMT driven Alzheimer's prediction model empowered with transfer learning and explainable AI approach in healthcare 5.0. Article Snippet: .. Author Year Dataset Details Method Adopted Max Accuracy (%) Limitations XAI 26 2021 UK Biobank SVM 84.26% Limited Data of diagnosed AD patients selected, Potential bias in documentation No 27 2022 OASIS, Kaggle DT, RF, SVM, XGBoost, Voting 83% Inconsistencies in raw data, computationally expensive No 28 2022 ADNI VGG-16, Scratch, ResNet-50 83.90% Overfitting risk No 29 2025 ADNI, CLAS SRNet, MRNet 81.2% Limited Imaging Modalities No 30 2022 ADNI DT, RF, SVM, LR, KNN, LSTM 76% − 92% Potential bias in feature selection, lack of external Validation No 33 2022 ADCC LR 81% Lack of Longitudinal tracking, age related confounding No 34 2022 ADNI RNN, LSTM 88.24% Poor performance for long-term tracking of AD No 35 2021 ADNI DNN 85.19% Lack of external Validation, potential overfitting of data No 36 2021 ADReSS SVM, RF, NN, NB, BERT 83.32% Small and Domain specific dataset, lack of external Validation No 37 2023 ADNI CNN with ResNet Backbone 83.27% Focuses only on mid-sagittal slices, Computational overhead No 39 2023 Kaggle CNN 87.36% Potential Overfitting of Data, Potential loss of 3D contextual info No 41 2022 Selection:Article Title: IoMT driven Alzheimer's prediction model empowered with transfer learning and explainable AI approach in healthcare 5.0. Article Snippet: .. Author Year Dataset Details Method Adopted Max Accuracy (%) Limitations XAI 26 2021 UK Biobank SVM 84.26% Limited Data of diagnosed AD patients selected, Potential bias in documentation No 27 2022 OASIS, Kaggle DT, RF, SVM, XGBoost, Voting 83% Inconsistencies in raw data, computationally expensive No 28 2022 ADNI VGG-16, Scratch, ResNet-50 83.90% Overfitting risk No 29 2025 ADNI, CLAS SRNet, MRNet 81.2% Limited Imaging Modalities No 30 2022 ADNI DT, RF, SVM, LR, KNN, LSTM 76% − 92% Potential bias in feature selection, lack of external Validation No 33 2022 ADCC LR 81% Lack of Longitudinal tracking, age related confounding No 34 2022 ADNI RNN, LSTM 88.24% Poor performance for long-term tracking of AD No 35 2021 ADNI DNN 85.19% Lack of external Validation, potential overfitting of data No 36 2021 ADReSS SVM, RF, NN, NB, BERT 83.32% Small and Domain specific dataset, lack of external Validation No 37 2023 ADNI CNN with ResNet Backbone 83.27% Focuses only on mid-sagittal slices, Computational overhead No 39 2023 Kaggle CNN 87.36% Potential Overfitting of Data, Potential loss of 3D contextual info No 41 2022 Biomarker Discovery:Article Title: IoMT driven Alzheimer's prediction model empowered with transfer learning and explainable AI approach in healthcare 5.0. Article Snippet: .. Author Year Dataset Details Method Adopted Max Accuracy (%) Limitations XAI 26 2021 UK Biobank SVM 84.26% Limited Data of diagnosed AD patients selected, Potential bias in documentation No 27 2022 OASIS, Kaggle DT, RF, SVM, XGBoost, Voting 83% Inconsistencies in raw data, computationally expensive No 28 2022 ADNI VGG-16, Scratch, ResNet-50 83.90% Overfitting risk No 29 2025 ADNI, CLAS SRNet, MRNet 81.2% Limited Imaging Modalities No 30 2022 ADNI DT, RF, SVM, LR, KNN, LSTM 76% − 92% Potential bias in feature selection, lack of external Validation No 33 2022 ADCC LR 81% Lack of Longitudinal tracking, age related confounding No 34 2022 ADNI RNN, LSTM 88.24% Poor performance for long-term tracking of AD No 35 2021 ADNI DNN 85.19% Lack of external Validation, potential overfitting of data No 36 2021 ADReSS SVM, RF, NN, NB, BERT 83.32% Small and Domain specific dataset, lack of external Validation No 37 2023 ADNI CNN with ResNet Backbone 83.27% Focuses only on mid-sagittal slices, Computational overhead No 39 2023 Kaggle CNN 87.36% Potential Overfitting of Data, Potential loss of 3D contextual info No 41 2022 Magnetic Resonance Imaging:Article Title: IoMT driven Alzheimer's prediction model empowered with transfer learning and explainable AI approach in healthcare 5.0. Article Snippet: .. Author Year Dataset Details Method Adopted Max Accuracy (%) Limitations XAI 26 2021 UK Biobank SVM 84.26% Limited Data of diagnosed AD patients selected, Potential bias in documentation No 27 2022 OASIS, Kaggle DT, RF, SVM, XGBoost, Voting 83% Inconsistencies in raw data, computationally expensive No 28 2022 ADNI VGG-16, Scratch, ResNet-50 83.90% Overfitting risk No 29 2025 ADNI, CLAS SRNet, MRNet 81.2% Limited Imaging Modalities No 30 2022 ADNI DT, RF, SVM, LR, KNN, LSTM 76% − 92% Potential bias in feature selection, lack of external Validation No 33 2022 ADCC LR 81% Lack of Longitudinal tracking, age related confounding No 34 2022 ADNI RNN, LSTM 88.24% Poor performance for long-term tracking of AD No 35 2021 ADNI DNN 85.19% Lack of external Validation, potential overfitting of data No 36 2021 ADReSS SVM, RF, NN, NB, BERT 83.32% Small and Domain specific dataset, lack of external Validation No 37 2023 ADNI CNN with ResNet Backbone 83.27% Focuses only on mid-sagittal slices, Computational overhead No 39 2023 Kaggle CNN 87.36% Potential Overfitting of Data, Potential loss of 3D contextual info No 41 2022 |