Journal: Diagnostics

Article Title: Mobile-HR: An Ophthalmologic-Based Classification System for Diagnosis of Hypertensive Retinopathy Using Optimized MobileNet Architecture

doi: 10.3390/diagnostics13081439

Figure Lengend Snippet: Limitations of the state-of-the-art-work on hypertensive retinopathy.

Article Snippet: Hacisoftaoglu et al. [ ] , This study uses a DL approach and the ResNet50 network to develop an autonomous detection model for smartphone-based retinal images. , EyePACS = 35,126 Images, Messidor = 1187 Images, Messidor-2 = 1748 Images , ACC = 91% SEN = 92% SPE = 90% , Only a few publicly available datasets were used to train and test the proposed model..

Techniques: Biomarker Discovery

Journal: PeerJ Computer Science

Article Title: Diagnosis of dengue virus infection using spectroscopic images and deep learning

doi: 10.7717/peerj-cs.985

Figure Lengend Snippet: The thematic diagram for ResNet101-TL architecture for DENV identification.

Article Snippet: We modified ResNet101 by replacing the mentioned layers with new layers of ‘FC_2’, ‘FC2_Softmax’ and ‘Class_output’ for our binary classes ‘Infected’, and ‘Healthy’ as depicted in .

Techniques:

Journal: PeerJ Computer Science

Article Title: Diagnosis of dengue virus infection using spectroscopic images and deep learning

doi: 10.7717/peerj-cs.985

Figure Lengend Snippet: The residual learning flow of ResNet101 architecture.

Article Snippet: We modified ResNet101 by replacing the mentioned layers with new layers of ‘FC_2’, ‘FC2_Softmax’ and ‘Class_output’ for our binary classes ‘Infected’, and ‘Healthy’ as depicted in .

Techniques:

Journal: PeerJ Computer Science

Article Title: Diagnosis of dengue virus infection using spectroscopic images and deep learning

doi: 10.7717/peerj-cs.985

Figure Lengend Snippet: Computational complexity comparison of ResNet101, DENV-TLDNN and SVM.

Article Snippet: We modified ResNet101 by replacing the mentioned layers with new layers of ‘FC_2’, ‘FC2_Softmax’ and ‘Class_output’ for our binary classes ‘Infected’, and ‘Healthy’ as depicted in .

Techniques: Comparison

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

Journal: Journal of Medical Internet Research

Article Title: Machine and Deep Learning for Tuberculosis Detection on Chest X-Rays: Systematic Literature Review

doi: 10.2196/43154

Figure Lengend Snippet: General characteristics of included studies.

Article Snippet: Kim et al [ ] , ChestX-ray14, Montgomery County, Shenzhen, Johns Hopkins Hospital , Culture (Johns Hopkins Hospital); radiologist’s reading , ResNet-50 and TBNet , TBNet on Johns Hopkins Hospital (AUC 0.87, sensitivity 85%, specificity 76%, positive predictive value 0.64, and negative predictive value 0.9) and Majority VoteTBNet and 2 radiologists (sensitivity 94%, specificity 85%, positive predictive value 0.76, and negative predictive value 0.96).

Techniques: Biomarker Discovery, Plasmid Preparation, Blocking Assay, Extraction, Modification, Software

Journal: Scientific Reports

Article Title: Automated detection of the HER2 gene amplification status in Fluorescence in situ hybridization images for the diagnostics of cancer tissues

doi: 10.1038/s41598-019-44643-z

Figure Lengend Snippet: Illustration of the two-stage deep learning detection system of the HER2 gene amplification stage in FISH images from breast cancer samples. ( A ) The nucleus detector network takes whole FISH images as input and outputs the localization and classification for all detected nuclei. ( B ) The signal detector network subsequently takes each detected nucleus and localizes and classifies individual FISH signals. The output of both networks is post-processed by calculation of the low/high grade ratios and HER2/CEN17 ratios, and an image-wide classification prediction is computed and reported. ( C ) Both detectors are based on RetinaNet which consists of a ResNet50 feature extraction network, a feature pyramid network and two fully convolutional classification and box regression networks for every level of the feature pyramid.

Article Snippet: In this work, we used the implementation of RetinaNet provided by Fizyr with a ResNet50 backbone provided by the Broad Institute.

Techniques: Amplification, Extraction