An automatic approach based on CNN architecture to detect Covid-19 disease from chest X-ray images

Novel coronavirus (COVID-19) is started from Wuhan (City in China), and is rapidly spreading among people living in other countries Today, around 215 countries are affected by COVID-19 disease WHO announced approximately number of cases 11,274,600 worldwide Due to rapidly rising cases daily in the hospitals, there are a limited number of resources available to control COVID-19 disease Therefore, it is essential to develop an accurate diagnosis of COVID-19 disease Early diagnosis of COVID-19 patients is important for preventing the disease from spreading to others In this paper, we proposed a deep learning based approach that can differentiate COVID- 19 disease patients from viral pneumonia, bacterial pneumonia, and healthy (normal) cases In this approach, deep transfer learning is adopted We used binary and multi-class dataset which is categorized in four types for experimentation: (i) Collection of 728 X-ray images including 224 images with confirmed COVID-19 disease and 504 normal condition images (ii) Collection of 1428 X-ray images including 224 images with confirmed COVID-19 disease, 700 images with confirmed common bacterial pneumonia, and 504 normal condition images (iii) Collections of 1442 X- ray images including 224 images with confirmed COVID-19 disease, 714 images with confirmed bacterial and viral pneumonia, and 504 images of normal conditions (iv) Collections of 5232 X- ray images including 2358 images with confirmed bacterial and 1345 with viral pneumonia, and 1346 images of normal conditions In this paper, we have used nine convolutional neural network based architecture (AlexNet, GoogleNet, ResNet-50, Se-ResNet-50, DenseNet121, Inception V4, Inception ResNet V2, ResNeXt-50, and Se-ResNeXt-50) Experimental results indicate that the pre trained model Se-ResNeXt-50 achieves the highest classification accuracy of 99 32% for binary class and 97 55% for multi-class among all pre-trained models © 2020, Springer Science+Business Media, LLC, part of Springer Nature