Journal: Multimedia Tools and Applications

Article Title: AI-based face mask detection system: a straightforward proposition to fight with Covid-19 situation

doi: 10.1007/s11042-022-13697-z

Figure Lengend Snippet: Review of the existing face mask detection techniques

Article Snippet: Shilpa Sethi et al. [ ] , ResNet50, AlexNet and MobileNet , Self-synthesized unbiased face mask dataset , ResNet50 is used as a proposed framework, and a transfer learning approach is applied. , It is not integrated into high-resolution video surveillance devices. , Achieved accuracy upto 98.2%.

Techniques: Produced

Journal: NPJ Digital Medicine

Article Title: Advanced and interpretable corneal staining assessment through fine grained knowledge distillation

doi: 10.1038/s41746-025-01706-y

Figure Lengend Snippet: The first row is the original raw image. The second row is the class activation mapping plus plus (CAM++) of the ResNet50. The third row is the fined-grained lesion detection visualization from the proposed FKD-CSS model. FKD fine-grained knowledge distillation. CSS corneal staining score.

Article Snippet: We trained a U-Net on the SUSTech-SYSU dataset utilizing a pre-trained ResNet50 backbone on ImageNet for automatic corneal segmentation.

Techniques: Activation Assay, Distillation, Staining

Journal: Scientific Reports

Article Title: Detection and analysis of COVID-19 in medical images using deep learning techniques

doi: 10.1038/s41598-021-99015-3

Figure Lengend Snippet: Results of CNN models without Fast.AI.

Article Snippet: Ayrton presented ResNet50 based deep transfer learning technique and reported the validation accuracy of 96.2% with a small dataset of 339 images for training and testing.Wang proposed five pre-trained deep learning models, which the Xception model showed a relatively ideal effect, and the accuracy reached 96.75%.

Techniques:

Journal: Scientific Reports

Article Title: Detection and analysis of COVID-19 in medical images using deep learning techniques

doi: 10.1038/s41598-021-99015-3

Figure Lengend Snippet: Results of CNN models with Fast.AI.

Article Snippet: Ayrton presented ResNet50 based deep transfer learning technique and reported the validation accuracy of 96.2% with a small dataset of 339 images for training and testing.Wang proposed five pre-trained deep learning models, which the Xception model showed a relatively ideal effect, and the accuracy reached 96.75%.

Techniques:

Journal: Scientific Reports

Article Title: Detection and analysis of COVID-19 in medical images using deep learning techniques

doi: 10.1038/s41598-021-99015-3

Figure Lengend Snippet: Model Fast.AI ResNet50 and Fast.AI ResNet152 for COVID/non-COVID on CT-scan dataset.

Article Snippet: Ayrton presented ResNet50 based deep transfer learning technique and reported the validation accuracy of 96.2% with a small dataset of 339 images for training and testing.Wang proposed five pre-trained deep learning models, which the Xception model showed a relatively ideal effect, and the accuracy reached 96.75%.

Techniques: Computed Tomography

Journal: Scientific Reports

Article Title: Detection and analysis of COVID-19 in medical images using deep learning techniques

doi: 10.1038/s41598-021-99015-3

Figure Lengend Snippet: Deep learning methods and techniques used in COVID-19.

Article Snippet: Ayrton presented ResNet50 based deep transfer learning technique and reported the validation accuracy of 96.2% with a small dataset of 339 images for training and testing.Wang proposed five pre-trained deep learning models, which the Xception model showed a relatively ideal effect, and the accuracy reached 96.75%.

Techniques: Labeling

Journal: Scientific Reports

Article Title: Detection and analysis of COVID-19 in medical images using deep learning techniques

doi: 10.1038/s41598-021-99015-3

Figure Lengend Snippet: Best result of our models for X-ray and CT scan images.

Article Snippet: Ayrton presented ResNet50 based deep transfer learning technique and reported the validation accuracy of 96.2% with a small dataset of 339 images for training and testing.Wang proposed five pre-trained deep learning models, which the Xception model showed a relatively ideal effect, and the accuracy reached 96.75%.

Techniques: Computed Tomography