Journal: PLOS ONE

Article Title: Accelerating antimicrobial peptide design: Leveraging deep learning for rapid discovery

doi: 10.1371/journal.pone.0315477

Figure Lengend Snippet: The architecture of the ResNet101.

Article Snippet: This paper utilized the ResNet101 model already incorporated in MATLAB® version 2022.

Techniques:

Journal: Diagnostics

Article Title: Classification of Monkeypox Images Using LIME-Enabled Investigation of Deep Convolutional Neural Network

doi: 10.3390/diagnostics13091639

Figure Lengend Snippet: Comparison of the suggested strategy against the current one to validate it.

Article Snippet: One of the top models, ResNet101 LIME, has the best MATLAB results, as seen in .

Techniques: Comparison, Modification

Journal: Diagnostics

Article Title: Classification of Monkeypox Images Using LIME-Enabled Investigation of Deep Convolutional Neural Network

doi: 10.3390/diagnostics13091639

Figure Lengend Snippet: Depicts architecture of ResNet101.

Article Snippet: One of the top models, ResNet101 LIME, has the best MATLAB results, as seen in .

Techniques:

Journal: Diagnostics

Article Title: Classification of Monkeypox Images Using LIME-Enabled Investigation of Deep Convolutional Neural Network

doi: 10.3390/diagnostics13091639

Figure Lengend Snippet: Performance of ResNet101.

Article Snippet: One of the top models, ResNet101 LIME, has the best MATLAB results, as seen in .

Techniques:

Journal: Diagnostics

Article Title: Classification of Monkeypox Images Using LIME-Enabled Investigation of Deep Convolutional Neural Network

doi: 10.3390/diagnostics13091639

Figure Lengend Snippet: The comparison of mean precision, mean sensitivity, mean specificity, mean accuracy and mean F-score over the 5-fold cross-validation.

Article Snippet: One of the top models, ResNet101 LIME, has the best MATLAB results, as seen in .

Techniques: Comparison

Journal: Diagnostics

Article Title: Classification of Monkeypox Images Using LIME-Enabled Investigation of Deep Convolutional Neural Network

doi: 10.3390/diagnostics13091639

Figure Lengend Snippet: Performance comparison of state-of-the-art method.

Article Snippet: One of the top models, ResNet101 LIME, has the best MATLAB results, as seen in .

Techniques: Comparison

Journal: Diagnostics

Article Title: Classification of Monkeypox Images Using LIME-Enabled Investigation of Deep Convolutional Neural Network

doi: 10.3390/diagnostics13091639

Figure Lengend Snippet: ( a – f ) Depicts the confusion matrices of monkeypox virus and other images for VGG-16, VGG-19, ResNet50, ResNet101, DenseNet201 and AlexNet, respectively.

Article Snippet: One of the top models, ResNet101 LIME, has the best MATLAB results, as seen in .

Techniques: Virus

Journal: Diagnostics

Article Title: Classification of Monkeypox Images Using LIME-Enabled Investigation of Deep Convolutional Neural Network

doi: 10.3390/diagnostics13091639

Figure Lengend Snippet: ( a – f ) Depicts the predicted probability scores of monkeypox virus and other images by VGG-16, VGG-19, ResNet50, ResNet101, DenseNet201 and AlexNet, respectively.

Article Snippet: One of the top models, ResNet101 LIME, has the best MATLAB results, as seen in .

Techniques: Virus

Journal: Diagnostics

Article Title: Intelligent Diagnosis and Classification of Keratitis

doi: 10.3390/diagnostics12061344

Figure Lengend Snippet: The stricter of ResNet101 [ 31 ].

Article Snippet: This paper used the pertained ResNet101 model already implemented in MATLAB ® version 2021.

Techniques:

Journal: Computational Intelligence and Neuroscience

Article Title: Gravitational Wave-Signal Recognition Model Based on Fourier Transform and Convolutional Neural Network

doi: 10.1155/2022/5892188

Figure Lengend Snippet: ResNet101 validation comparison.

Article Snippet: Additionally, the Resnet101 model, developed on the Baidu EasyDL platform, is adopted as a comparative model. Our average recognition accuracy performs approximately 4% better than the Resnet101 model. Based on the excellent performance of convolutional neural network in the field of image recognition, this paper studies the characteristics of gravitational wave signals and obtains a more appropriate recognition model after training and tuning, in order to achieve the purpose of automatic recognition of whether the signal data contain real gravitational wave signals.

Techniques: Biomarker Discovery, Comparison