resnet50-softmax  (SoftMax Inc)

Journal: Journal of Imaging

Article Title: Residual-Based Multi-Stage Deep Learning Framework for Computer-Aided Alzheimer’s Disease Detection

doi: 10.3390/jimaging10060141

Figure Lengend Snippet: Brief summary of the existing models.

Article Snippet: They implemented the models ResNet50+ SoftMax, ResNet50+SVM, and ResNet50+RF and reported accuracies of 99.00%, 92.00%, and 85.70%, respectively.

Techniques:

Journal: Journal of Imaging

Article Title: Residual-Based Multi-Stage Deep Learning Framework for Computer-Aided Alzheimer’s Disease Detection

doi: 10.3390/jimaging10060141

Figure Lengend Snippet: The experimental evaluation results of the proposed model with SOTA algorithms using ADNI-1 dataset.

Article Snippet: They implemented the models ResNet50+ SoftMax, ResNet50+SVM, and ResNet50+RF and reported accuracies of 99.00%, 92.00%, and 85.70%, respectively.

Techniques:

Journal: Journal of Imaging

Article Title: Residual-Based Multi-Stage Deep Learning Framework for Computer-Aided Alzheimer’s Disease Detection

doi: 10.3390/jimaging10060141

Figure Lengend Snippet: The experimental evaluation results of the proposed model with SOTA algorithms using the MIRIAD dataset.

Article Snippet: They implemented the models ResNet50+ SoftMax, ResNet50+SVM, and ResNet50+RF and reported accuracies of 99.00%, 92.00%, and 85.70%, respectively.

Techniques:

Journal: Sensors (Basel, Switzerland)

Article Title: Real-Time Robotic Presentation Skill Scoring Using Multi-Model Analysis and Fuzzy Delphi–Analytic Hierarchy Process

doi: 10.3390/s23249619

Figure Lengend Snippet: Summary of the existing approaches used for basic and learning-centred emotion classification models.

Article Snippet: [ ] , × , √ , × , 0 , 0 , ResNet50 , Softmax , × , CNN Model.

Techniques: Extraction, Plasmid Preparation

Journal: Sensors (Basel, Switzerland)

Article Title: Brain MRI Analysis for Alzheimer’s Disease Diagnosis Using CNN-Based Feature Extraction and Machine Learning

doi: 10.3390/s22082911

Figure Lengend Snippet: Summary and comparison of the selected recent research.

Article Snippet: Proposed model ADNI [ ] , ResNet50 + Softmax , 99% , 98% , 99%.

Techniques: Comparison, Selection, T-Test, Generated

Journal: Sensors (Basel, Switzerland)

Article Title: Brain MRI Analysis for Alzheimer’s Disease Diagnosis Using CNN-Based Feature Extraction and Machine Learning

doi: 10.3390/s22082911

Figure Lengend Snippet: Training and validation performance of ResNet50-Softmax.

Article Snippet: Proposed model ADNI [ ] , ResNet50 + Softmax , 99% , 98% , 99%.

Techniques: Biomarker Discovery

Journal: Sensors (Basel, Switzerland)

Article Title: Brain MRI Analysis for Alzheimer’s Disease Diagnosis Using CNN-Based Feature Extraction and Machine Learning

doi: 10.3390/s22082911

Figure Lengend Snippet: Performance of the three classifiers in the proposed model.

Article Snippet: Proposed model ADNI [ ] , ResNet50 + Softmax , 99% , 98% , 99%.

Techniques:

Journal: Sensors (Basel, Switzerland)

Article Title: Brain MRI Analysis for Alzheimer’s Disease Diagnosis Using CNN-Based Feature Extraction and Machine Learning

doi: 10.3390/s22082911

Figure Lengend Snippet: Confusion matrix obtained on the test dataset with Resnet50-Softmax.

Article Snippet: Proposed model ADNI [ ] , ResNet50 + Softmax , 99% , 98% , 99%.

Techniques:

Journal: Sensors (Basel, Switzerland)

Article Title: Brain MRI Analysis for Alzheimer’s Disease Diagnosis Using CNN-Based Feature Extraction and Machine Learning

doi: 10.3390/s22082911

Figure Lengend Snippet: Resnet50-Softmax experiment results with the ADNI dataset.

Article Snippet: Proposed model ADNI [ ] , ResNet50 + Softmax , 99% , 98% , 99%.

Techniques:

Journal: Sensors (Basel, Switzerland)

Article Title: Brain MRI Analysis for Alzheimer’s Disease Diagnosis Using CNN-Based Feature Extraction and Machine Learning

doi: 10.3390/s22082911

Figure Lengend Snippet: Resnet50-Softmax experiment results with the MIRIAD dataset.

Article Snippet: Proposed model ADNI [ ] , ResNet50 + Softmax , 99% , 98% , 99%.

Techniques:

Journal: Sensors (Basel, Switzerland)

Article Title: Brain MRI Analysis for Alzheimer’s Disease Diagnosis Using CNN-Based Feature Extraction and Machine Learning

doi: 10.3390/s22082911

Figure Lengend Snippet: Comparison of our test performance with eight existing state-of-the-art methods.

Article Snippet: Proposed model ADNI [ ] , ResNet50 + Softmax , 99% , 98% , 99%.

Techniques: Comparison