Brief Review — Deep-COVID: Predicting COVID-19 from chest X-ray images using deep transfer learning
Deep-COVID: Predicting COVID-19 from chest X-ray images using deep transfer learning,
Deep-COVID, by Snap Inc., Isfahan University of Medical Sciences, The University of Iowa, and Isfahan University of Technology
2020 J. MEDIA, Over 650 Citations (Sik-Ho Tsang @ Medium)
Image Classification, Medical Image Analysis, Medical Imaging
- A dataset of 5000 Chest X-rays is prepared from the publicly available datasets. Images exhibiting COVID-19 disease presence were identified by board-certified radiologist.
- Transfer learning on a subset of 2000 radiograms was used to train four popular convolutional neural networks, including ResNet18, ResNet50, SqueezeNet, and DenseNet-121, to identify COVID-19 disease in the analyzed chest X-ray images.
- COVID-Xray-5k Dataset
- Model Architectures
1. COVID-Xray-5k Dataset
The above shows 3 COVID-19 images. (To me, the characteristics is subtle.)
Since the number of Non-Covid images was very small in the (https://github.com/ieee8023/covid-chestxray-dataset) dataset, additional images were employed from the CheXpert dataset.
Finally, COVID-Xray-5k dataset is formed, as above.
All models have a similar performance according to the AUC with the SqueezeNet achieving a slightly higher AUC than the other models. It is worth mentioning that for highly imbalanced test sets.
The confidence interval of specificity rates are small (around 1%), since we have around 3000 samples for this class, whereas for the sensitivity rate, a slightly higher confidence interval (around 2.7%) is obtained because of the limited number of samples.
- The likely regions of COVID-19 disease marked by the board-certified radiologist are shown on the last row.
The generated heatmaps show a good agreement with the radiologist-determined regions of the COVID-19 disease.
A brief paper review today, Happy Boxing Day !!!
[2020 J. MEDIA] [Deep-COVID]
Deep-COVID: Predicting COVID-19 from chest X-ray images using deep transfer learning