Comprehensive Analysis of Deep Learning Methods for COVID-19 Detection and Classification

The progress of precise recognition techniques and the evaluation of genetic connections are required because of the worldwide spread of COVID-19. As conventional techniques for COVID-19 detection are time-consuming and require accurate variables in order to minimize or cut short errors that could possibly occur, the COVID-19 epidemic's necessity for early diagnosis and surveillance led to the deployment of Deep Learning (DL) technology for efficient evaluation. Deep learning is a branch of artificial intelligence (AI) and Machine learning (ML), and due to its remarkable ability to assimilate new information, DL technology has quickly risen to prominence. Many DL architectures, like RNN, CNN, and long-term memory, can be utilized for forecasting, perdition, and classification of COVID-19. The intention of this paper is to conduct a comprehensive analysis of various deep learning methods, for detecting and classifying the COVID-19 virus using diverse data sources, such as chest X-rays, CT scans, and genomic sequences, to highlight their exceptional accuracy in identification. We also explore DL's involvement in analyzing the virus's genetic code, providing insight into how it evolved. It aims to deliver a summary of earlier research and its COVID-19 applications.