A deep learning based approach for automatic detection of COVID-19 cases using chest X-ray images - Bhattacharyya, Abhijit; Bhaik, Divyanshu; Kumar, Sunil; Thakur, Prayas; Sharma, Rahul; Pachori, Ram Bilas (Biomedical signal processing and control, 2022)
•This work presents a comprehensive study on the classification of COVID-19, pneumonia, and normal X-ray images.•Conditional generative adversarial network (C-GAN) is used to obtain segmented lung images.•Deep learning models are employed for computing discriminatory features.•The method also computes key-point features using SIFT and BRISK algorithms.•Performance comparison of different machine learning methods is presented. In this global pandemic situation of coronavirus disease (COVID-19), it is of foremost priority to look up efficient and faster diagnosis methods for reducing the transmission rate of the virus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Recent research has indicated that radio-logical images carry essential information about the COVID-19 virus. Therefore, artificial intelligence (AI) assisted automated detection of lung infections may serve as a potential diagnostic tool. It can be augmented with conventional medical tests for tackling COVID-19. In this paper, we propose a new method for detecting COVID-19 and pneumonia using chest X-ray images. The proposed method can be described as a three-step process. The first step includes the segmentation of the raw X-ray images using the conditional generative adversarial network (C-GAN) for obtaining the lung images. In the second step, we feed the segmented lung images into a novel pipeline combining key points extraction methods and trained deep neural networks (DNN) for extraction of discriminatory features. Several machine learning (ML) models are employed to classify COVID-19, pneumonia, and normal lung images in the final step. A comparative analysis of the classification performance is carried out among the different proposed architectures combining DNNs, key point extraction methods, and ML models. We have achieved the highest testing classification accuracy of 96.6% using the VGG-19 model associated with the binary robust invariant scalable key-points (BRISK) algorithm. The proposed method can be efficiently used for screening of COVID-19 infected patients.