Journal: Neural Computing & Applications

Article Title: VGGCOV19-NET: automatic detection of COVID-19 cases from X-ray images using modified VGG19 CNN architecture and YOLO algorithm

doi: 10.1007/s00521-022-06918-x

Figure Lengend Snippet: Comparison of the recommended VGGCOV19-NET COVID-19 diagnosis method with other CNN methods developed using radiology images

Article Snippet: Khan et al. [ ] classified three classes with a new Xception CNN architecture-based CNN model with an accuracy ratio of 95%, Ahammed et al. [ ] classified three classes with the deep CNN model they created with accuracy of 94%, Apostolopoulos and Mpesiana [ ] classified three classes with the pre-trained VGG19 architecture with accuracy of 93.48%, Aslan et al. [ ] classified three classes with the hybrid CNN architecture with accuracy of 98.70%, Hira et al. [ ] classified three classes with the specially ResNeXt-50 with accuracy of 97.55%, and Wang et al. [ ] classified three classes with the deep CNN model they called COVID-Net with an accuracy of 93.3%.

Techniques: Comparison, Biomarker Discovery, Modification, Imaging

Journal: Heliyon

Article Title: Introducing a novel dataset for facial emotion recognition and demonstrating significant enhancements in deep learning performance through pre-processing techniques

doi: 10.1016/j.heliyon.2024.e38913

Figure Lengend Snippet: EfficientnetB7_CNN model architecture.

Article Snippet: To leverage (pre-trained) models accessible on the Kaggle platform, we propose a new architecture based on the EfficientNetB7 model. We initialize the base model with pre-trained weights from ImageNet and exclude the top classification layer to enable customization for our specific task.

Techniques:

Journal: Heliyon

Article Title: Introducing a novel dataset for facial emotion recognition and demonstrating significant enhancements in deep learning performance through pre-processing techniques

doi: 10.1016/j.heliyon.2024.e38913

Figure Lengend Snippet: Evaluation of the models using FER13 and FER24_CK + datasets.

Article Snippet: To leverage (pre-trained) models accessible on the Kaggle platform, we propose a new architecture based on the EfficientNetB7 model. We initialize the base model with pre-trained weights from ImageNet and exclude the top classification layer to enable customization for our specific task.

Techniques:

Journal: Heliyon

Article Title: Introducing a novel dataset for facial emotion recognition and demonstrating significant enhancements in deep learning performance through pre-processing techniques

doi: 10.1016/j.heliyon.2024.e38913

Figure Lengend Snippet: EfficientNetB7-CNN (implementation parameters).

Article Snippet: To leverage (pre-trained) models accessible on the Kaggle platform, we propose a new architecture based on the EfficientNetB7 model. We initialize the base model with pre-trained weights from ImageNet and exclude the top classification layer to enable customization for our specific task.

Techniques:

Journal: Heliyon

Article Title: Introducing a novel dataset for facial emotion recognition and demonstrating significant enhancements in deep learning performance through pre-processing techniques

doi: 10.1016/j.heliyon.2024.e38913

Figure Lengend Snippet: Confusion matrix of EfficientNetB7-CNN for FER task on FER24-CK+ (7 classes) private testing.

Article Snippet: To leverage (pre-trained) models accessible on the Kaggle platform, we propose a new architecture based on the EfficientNetB7 model. We initialize the base model with pre-trained weights from ImageNet and exclude the top classification layer to enable customization for our specific task.

Techniques:

Journal: Heliyon

Article Title: Introducing a novel dataset for facial emotion recognition and demonstrating significant enhancements in deep learning performance through pre-processing techniques

doi: 10.1016/j.heliyon.2024.e38913

Figure Lengend Snippet: Outlines the EfficientNetB7-CNN performance measure for private testing.

Article Snippet: To leverage (pre-trained) models accessible on the Kaggle platform, we propose a new architecture based on the EfficientNetB7 model. We initialize the base model with pre-trained weights from ImageNet and exclude the top classification layer to enable customization for our specific task.

Techniques:

Journal: Heliyon

Article Title: Introducing a novel dataset for facial emotion recognition and demonstrating significant enhancements in deep learning performance through pre-processing techniques

doi: 10.1016/j.heliyon.2024.e38913

Figure Lengend Snippet: State-of-the-art comparison of models’ accuracy using the FER13 dataset as a base.

Article Snippet: To leverage (pre-trained) models accessible on the Kaggle platform, we propose a new architecture based on the EfficientNetB7 model. We initialize the base model with pre-trained weights from ImageNet and exclude the top classification layer to enable customization for our specific task.

Techniques: Comparison