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Summary and comparison of the selected recent research.

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: Bäckström et al. (2018) [ ] , 3D ConvNet + Softmax , 96% , , .

Techniques: Comparison, Selection, T-Test, Generated

Comparison of our test performance with eight existing state-of-the-art methods.

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: Bäckström et al. (2018) [ ] , 3D ConvNet + Softmax , 96% , , .

Techniques: Comparison

Performance for all categories using the proposed method (ConvNet & LRBSF). Best and worst performance of individual participant is also mentioned.

Journal: PLoS ONE

Article Title: Electroencephalogram-based decoding cognitive states using convolutional neural network and likelihood ratio based score fusion

doi: 10.1371/journal.pone.0178410

Figure Lengend Snippet: Performance for all categories using the proposed method (ConvNet & LRBSF). Best and worst performance of individual participant is also mentioned.

Article Snippet: Because ConvNet is a complete framework, most of the studies have used the ConvNet classifier (softmax) for prediction/classification [ ].

Techniques: Selection

Significant difference (p-value) of accuracies between the proposed and other methods.

Journal: PLoS ONE

Article Title: Electroencephalogram-based decoding cognitive states using convolutional neural network and likelihood ratio based score fusion

doi: 10.1371/journal.pone.0178410

Figure Lengend Snippet: Significant difference (p-value) of accuracies between the proposed and other methods.

Article Snippet: Because ConvNet is a complete framework, most of the studies have used the ConvNet classifier (softmax) for prediction/classification [ ].

Techniques: