COVID-19 Classification from Chest X-Ray Images: A Framework of Deep Explainable Artificial Intelligence.

COVID-19 detection and classification using chest X-ray images is a current hot research topic based on the important application known as medical image analysis. To halt the spread of COVID-19, it is critical to identify the infection as soon as possible. Due to time constraints and the expertise of radiologists, manually diagnosing this infection from chest X-ray images is a difficult and time-consuming process. Artificial intelligence techniques have had a significant impact on medical image analysis and have also introduced several techniques for COVID-19 diagnosis. Deep learning and explainable AI have shown significant popularity among AL techniques for COVID-19 detection and classification. In this work, we propose a deep learning and explainable AI technique for the diagnosis and classification of COVID-19 using chest X-ray images. Initially, a hybrid contrast enhancement technique is proposed and applied to the original images that are later utilized for the training of two modified deep learning models. The deep transfer learning concept is selected for the training of pretrained modified models that are later employed for feature extraction. Features of both deep models are fused using improved canonical correlation analysis that is further optimized using a hybrid algorithm named Whale-Elephant Herding. Through this algorithm, the best features are selected and classified using an extreme learning machine (ELM). Moreover, the modified deep models are utilized for Grad-CAM visualization. The experimental process was conducted on three publicly available datasets and achieved accuracies of 99.1, 98.2, and 96.7%, respectively. Moreover, the ablation study was performed and showed that the proposed accuracy is better than the other methods.

Viruses as tools in gene therapy, vaccine development, and cancer treatment.

Using viruses to our advantage has been a huge leap for humanity. Their ability to mediate horizontal gene transfer has made them useful tools for gene therapy, vaccine development, and cancer treatment. Adenoviruses, adeno-associated viruses, retroviruses, lentiviruses, alphaviruses, and herpesviruses are a few of the most common candidates for use as therapeutic agents or efficient gene delivery systems. Efforts are being made to improve and perfect viral-vector-based therapies to overcome potential or reported drawbacks. Some preclinical trials of viral vector vaccines have yielded positive results, indicating their potential as prophylactic or therapeutic vaccine candidates. Utilization of the oncolytic activity of viruses is the future of cancer therapy, as patients will then be free from the harmful effects of chemo- or radiotherapy. This review discusses in vitro and in vivo studies showing the brilliant therapeutic potential of viruses.