COVID-19 Chest CT image Augmentation GAN Dataset

In this Dataet, we introduce DTL models to classify limited COVID-19 chest CT scan digital images. To input adopting CT images of the chest to the DCNN, we enriched the medical chest CT images using classical data augmentation and CGAN to generate more CT images. After that, a classifier is used to ensemble the class (COVID/NonCOVID) outputs of the classification outcomes. The proposed DTL models were evaluated on the COVID-19 CT scan images dataset. The novelty of this research is conducted as follows: (1) The introduced DTL models have end-to-end structure without classical feature extraction and selection methods. (2) We show that data augmentation and conditional generative adversarial network (CGAN) is an effective technique to generate CT images. (3) Chest CT images are one of the best tools for the classification of COVID-19. (4) The DTL models have been shown to yield very high accuracy in the limited COVID-19 dataset.Content There are 742 CT images and 2 categories (COVID/NonCOVID). Dataset |Train | Validation | Test COVID NonCOVID COVID NonCOVID COVID NonCOVID COVID-19 191 234 60 58 94 105 COVID-19 + Aug 2292 2808 720 696 94 105 COVID-19 + CGAN 2191 2234 210 208 94 105 COVID-19 + Aug + CGAN 4292 4808 870 846 94 105Acknowledgements Cite our papers: Loey, M., Manogaran, G. & Khalifa, N.E.M. A deep transfer learning model with classical data augmentation and CGAN to detect COVID-19 from chest CT radiography digital images. Neural Comput & Applic (2020). https://doi.org/10.1007/s00521-020-05437-x Loey, Mohamed; Smarandache, Florentin; M. Khalifa, Nour E. 2020. "Within the Lack of Chest COVID-19 X-ray Dataset: A Novel Detection Model Based on GAN and Deep Transfer Learning" Symmetry 12, no. 4: 651. https://doi.org/10.3390/sym12040651 Khalifa, N.E.M., Smarandache, F., Manogaran, G. et al. A Study of the Neutrosophic Set Significance on Deep Transfer Learning Models: an Experimental Case on a Limited COVID-19 Chest X-ray Dataset. Cogn Comput (2021). https://doi.org/10.1007/s12559-020-09802-9