A Deep Learning Framework for Coronavirus Disease (COVID-19) Detection in X-Ray Images
"In this study, a grid search (GS) and pre-trained model aided convolutional neural network (CNN) model is proposed to detect COVID-19 in X-Ray images. In the proposed method, the GS method is employed to optimize the hyperparameters of CNN, which directly affects classification performance. Three pre-trained CNN models (GoogleNet, ResNet18 and ResNet50), which can be used for classification, feature extraction and transfer learning purposes were used for transfer learning."
"Since the COVID-19 appeared in December 2019, it is quite difficult to obtain a public dataset for research work. Therefore, the data was obtained from different sources [1-2] and hospital [3] shares."
According to the results ResNet50 aided proposed method is the most successfull one toward others (with %97.69 success rate).
The code and paper (preprint or published version) will be available soon.
[1] Cohen, J.P., Morrison, P., Dao, L.: Covid-19 image data collection. arXiv 2003.11597 (2020). URL https://github.com/ieee8023/covid-chestxray-dataset
[2] Paul Mooney: Chest X-Ray Images (Pneumonia). https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia. Online; accessed 20 March 2020
[3] Societa Italiana di Radiologia Medica e Interventistica:COVID-19 Database. https://www.sirm.org. Online;accessed 28 March 2020