A comprehensive review of analyzing the chest X-ray images to detect COVID-19 infections using deep learning techniques.

COVID-19, a highly infectious respiratory disease a used by SARS virus, has killed millions of people across many countries. To enhance quick and accurate diagnosis of COVID-19, chest X-ray (CXR) imaging methods were commonly utilized. Identifying the infection manually by radio imaging, on the other hand, was considered, extremely difficult due to the time commitment and significant risk of human error. Emerging artificial intelligence (AI) techniques promised exploration in the development of precise and as well as automated COVID-19 detection tools. Convolution neural networks (CNN), a well performing deep learning strategy tends to gain substantial favors among AI approaches for COVID-19 classification. The preprints and published studies to diagnose COVID-19 with CXR pictures using CNN and other deep learning methodologies are reviewed and critically assessed in this research. This study focused on the methodology, algorithms, and preprocessing techniques used in various deep learning architectures, as well as datasets and performance studies of several deep learning architectures used in prediction and diagnosis. Our research concludes with a list of future research directions in COVID-19 imaging categorization.

A deep transfer learning-based convolution neural network model for COVID-19 detection using computed tomography scan images for medical applications.

The Coronavirus (COVID-19) has become a critical and extreme epidemic because of its international dissemination. COVID-19 is the world's most serious health, economic, and survival danger. This disease affects not only a single country but the entire planet due to this infectious disease. Illnesses of Covid-19 spread at a much faster rate than usual influenza cases. Because of its high transmissibility and early diagnosis, it isn't easy to manage COVID-19. The popularly used RT-PCR method for COVID-19 disease diagnosis may provide false negatives. COVID-19 can be detected non-invasively using medical imaging procedures such as chest CT and chest x-ray. Deep learning is the most effective machine learning approach for examining a considerable quantity of chest computed tomography (CT) pictures that can significantly affect Covid-19 screening. Convolutional neural network (CNN) is one of the most popular deep learning techniques right now, and its gaining traction due to its potential to transform several spheres of human life. This research aims to develop conceptual transfer learning enhanced CNN framework models for detecting COVID-19 with CT scan images. Though with minimal datasets, these techniques were demonstrated to be effective in detecting the presence of COVID-19. This proposed research looks into several deep transfer learning-based CNN approaches for detecting the presence of COVID-19 in chest CT images.VGG16, VGG19, Densenet121, InceptionV3, Xception, and Resnet50 are the foundation models used in this work. Each model's performance was evaluated using a confusion matrix and various performance measures such as accuracy, recall, precision, f1-score, loss, and ROC. The VGG16 model performed much better than the other models in this study (98.00 % accuracy). Promising outcomes from experiments have revealed the merits of the proposed model for detecting and monitoring COVID-19 patients. This could help practitioners and academics create a tool to help minimal health professionals decide on the best course of therapy.

Impact of water depth on thermal efficiency, exergy efficiency, and exergy losses of finned acrylic solar still: an experimental study.

This paper presents a complete exergy analysis and exergy destruction of a finned acrylic solar still (SS) at 1, 2, and 3 cm salt water depth (Wd). The coefficients of heat transfer of salt water-glass have been computed for evaporative, convective, and radiant heat transfer. Also, thermal efficiency, exergy loss of basin, saltwater, and glass was determined. The maximum hourly output of a finned acrylic SS at 1, 2, and 3 cm Wd was1.23, 0.93, and 0.81 kg, respectively. The daily yield of 5.67, 5.16, and 4.41 kg was collected from the finned acrylic SS at 1, 2, and 3 cm salt Wd, respectively. For the finned acrylic SS at 1 cm Wd, the maximal exergy loss of the basin, saltwater, and glass was 604.3, 92.8, and 141.8 W/m2, respectively. The thermal and exergy efficiency of the finned acrylic SS at 1 cm Wd is 42.54 and 3.83%, respectively, while at 2 cm salt Wd, it is 37.92 and 3.22% and for 3 cm Wd is 31.2 and 2.7%. According to the findings, the exergy loss of saltwater in finned acrylic SS at 1 cm Wd is minimal when compared to the exergy loss of saltwater in finned acrylic SS at 2 and 3 cm Wd.