COVID-19, caused by SARS-CoV-2, has been declared as a global pandemic by WHO. Early diagnosis of COVID-19 patients may reduce the impact of coronavirus using modern computational methods like deep learning. Various deep learning models based on CT and chest X-ray images are studied and compared in this study as an alternative solution to reverse transcription-polymerase chain reactions. This study consists of three stages: planning, conduction, and analysis/reporting. In the conduction stage, inclusion and exclusion criteria are applied to the literature searching and identification. Then, we have implemented quality assessment rules, where over 75 scored articles in the literature were included. Finally, in the analysis/reporting stage, all the papers are reviewed and analysed. After the quality assessment of the individual papers, this study adopted 57 articles for the systematic literature review. From these reviews, the critical analysis of each paper, including the represented matrix for the model evaluation, existing contributions, and motivation, has been tracked with suitable illustrations. We have also interpreted several insights of each paper with appropriate annotation. Further, a set of comparisons has been enumerated with suitable discussion. Convolutional neural networks are the most commonly used deep learning architecture for COVID-19 disease classification and identification from X-ray and CT images. Various prior studies did not include data from a hospital setting nor did they consider data preprocessing before training a deep learning model.

Background: The rise of COVID-19 has caused immeasurable loss to public health globally. The world has faced a severe shortage of the goldstandard testing kit known as RT-PCR (Reverse Transcription Polymerase Chain Reaction). The accuracy of RT-PCR is not 100%, and it takes a few hours to deliver the test results. An additional testing solution to RT-PCR would be beneficial. Deep learning's superiority in image processing is characterized as the most effective COVID-19 diagnosis based on images. The small number of COVID-19 X-ray images in existing deep-learning methods for COVID-19 diagnosis may degrade the performance of deep-learning methods for new sets of images. Our priority for this research is to test and compare different deep-learning algorithms on a dataset consisting of many COVID-19 X-ray images. Methods: We have merged the publicly available image data into two groups (COVID and Normal). Our dataset contains 579 COVID-19 cases and 1773 Normal cases of X-ray images. We have used 145 COVID-19 cases and 150 Normal cases to test the deep-learning models. Deeplearning models based on CNN, VGG16 and 19, and InceptionV3 have been considered for prediction. The performance of these models is compared based on measurements of accuracy, sensitivity, and specificity. In the deep-learning models, the SoftMax activation function is used along with the Adam optimiser and categorical cross-entropy loss. A customised hybrid CNN model found in literature is considered and compared to explore how the inclusion of many COVID-19 X-ray images could impact the model's performance. Results: The accuracy of the considered deep-learning models using InceptionV3, VGG16, and VGG19 algorithms achieved 50%, 90%, and 83%, respectively, in predicting the X-ray images of COVID-19. We have shown that number of COVID-19 X-ray images does have a significant Shah Siddiqui et al.impact on the model's performance. A customised hybrid CNN model found in the literature failed to perform well on a dataset consisting of a large number of COVID-19 X-ray images. The customised hybrid CNN model reached an accuracy of 71% on many COVID-19 X-ray images. In contrast, it achieved 98% accuracy on a small number of COVID-19 X-ray images. It is also observed from the experiments that the VGG16 performs well with an increased number of images. Conclusions: A maximized number of COVID-19 X-ray images should be considered in building a deep-learning model. The deep-learning model with VGG16 performs the best in predicting from the X-ray images.

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This project aims to create a real time health advice platform and telemedicine system that can reach healthcare providers and healthcare deprived people. A pragmatic approach is being used to understand the research problem of this study, which allows all the authors to recognize diverse concepts and clarifications, as well as understanding the research problem and quality. The initial primary research has consisted of three sections: planning, configuration, and reporting. We have identified the users and modelled our system based on their geographical location, age groups, literacy, and diseases. We also identified that some of the continents are far behind in health information technology research, where 37% of work is carried out in the USA, 24% in Europe, 15% in Asia, 3% in Africa and 15 % in the rest of the world. We have further tracked out that most of the low and middle income countries' populations have less technological knowledge, where 60-70% are uneducated, 20-30% are educated, and only 10% have higher education and experience. We have considered these data sets and developed a sample web, IOS and Android based telemedicine platform that includes various functionalities including video and instant messaging, and social and educational posts for all types of users, e.g., doctors, nurses, and patients.

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