Coronavirus (COVID-19) is a viral disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The spread of COVID-19 seems to have a detrimental effect on the global economy and health. A positive chest X-ray of infected patients is a crucial step in the battle against COVID-19. Early results suggest that abnormalities exist in chest X-rays of patients suggestive of COVID-19. This has led to the introduction of a variety of deep learning systems and studies have shown that the accuracy of COVID-19 patient detection through the use of chest X-rays is strongly optimistic. Deep learning networks like convolutional neural networks (CNNs) need a substantial amount of training data. Because the outbreak is recent, it is difficult to gather a significant number of radiographic images in such a short time. Therefore, in this research, we present a method to generate synthetic chest X-ray (CXR) images by developing an Auxiliary Classifier Generative Adversarial Network (ACGAN) based model called CovidGAN. In addition, we demonstrate that the synthetic images produced from CovidGAN can be utilized to enhance the performance of CNN for COVID-19 detection. Classification using CNN alone yielded 85% accuracy. By adding synthetic images produced by CovidGAN,the accuracy increased to 95%. We hope this method will speed up COVID-19 detection and lead to more robust systems of radiology. INDEX TERMS Deep learning, convolutional neural networks, generative adversarial networks, synthetic data augmentation, COVID-19 detection.

Pneumonia is among the top diseases which cause most of the deaths all over the world. Virus, bacteria and fungi can all cause pneumonia. However, it is difficult to judge the pneumonia just by looking at chest X-rays. The aim of this study is to simplify the pneumonia detection process for experts as well as for novices. We suggest a novel deep learning framework for the detection of pneumonia using the concept of transfer learning. In this approach, features from images are extracted using different neural network models pretrained on ImageNet, which then are fed into a classifier for prediction. We prepared five different models and analyzed their performance. Thereafter, we proposed an ensemble model that combines outputs from all pretrained models, which outperformed individual models, reaching the state-of-the-art performance in pneumonia recognition. Our ensemble model reached an accuracy of 96.4% with a recall of 99.62% on unseen data from the Guangzhou Women and Children's Medical Center dataset.

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At present days, DR becomes a more common disease affecting the eyes because of drastic rise in the glucose level of blood. Almost half of the people under the age of 70's get severely affected due to diabetes. The earlier recognition and proper medication results to loss of vision in several DR patients. When the warning signs are identified, the severity level of the disease has to be validated to take decisions regarding the proper treatment. The current research focuses on the concept of classifying the images of DR fundus based on the severity level using a deep learning model. This paper proposes a deep learning based automated detection and classification model for fundus diabetic retinopathy (DR) images. The proposed method involves several processes namely preprocessing, segmentation and classification. Initially, preprocessing stage is carried out to get rid of the unnecessary noise exist in the edges. Next, histogram based segmentation takes place to extract the useful regions from the image. Then, synergic deep learning (SDL) model is applied to classify DR fundus images to various severity levels. The justification of the presented SDL model is carried out on Messidor DR dataset. The experimentation indicated that the presented SDL model offers better classification over the existing models.