ENResNet: A novel residual neural network for chest X-ray enhancement based COVID-19 detection

•Deep residual neural network based chest x-ray image enhancement (ENResNet) approach.•Residual images are used to train the model.•Eight residual modules created for feature map from patch images.•High accuracy is achieved from simple CNN for COVID-19 detection.•Efficiently differentiate COVID-19 from usual pneumonia with 98.4% accuracy. Recently, people around the world are being vulnerable to the pandemic effect of the novel Corona Virus. It is very difficult to detect the virus infected chest X-ray (CXR) image during early stages due to constant gene mutation of the virus. It is also strenuous to differentiate between the usual pneumonia from the COVID-19 positive case as both show similar symptoms. This paper proposes a modified residual network based enhancement (ENResNet) scheme for the visual clarification of COVID-19 pneumonia impairment from CXR images and classification of COVID-19 under deep learning framework. Firstly, the residual image has been generated using residual convolutional neural network through batch normalization corresponding to each image. Secondly, a module has been constructed through normalized map using patches and residual images as input. The output consisting of residual images and patches of each module are fed into the next module and this goes on for consecutive eight modules. A feature map is generated from each module and the final enhanced CXR is produced via up-sampling process. Further, we have designed a simple CNN model for automatic detection of COVID-19 from CXR images in the light of ‘multi-term loss’ function and ‘softmax’ classifier in optimal way. The proposed model exhibits better result in the diagnosis of binary classification (COVID vs. Normal) and multi-class classification (COVID vs. Pneumonia vs. Normal) in this study. The suggested ENResNet achieves a classification accuracy 99.7% and 98.4% for binary classification and multi-class detection respectively in comparison with state-of-the-art methods.