Application of Deep Learning for Detection and Analysis of COVID-19 Outcomes: Deep-COVID - Muhammad Ibrahim Khalil; Mahwish Kundi; Saif Ur Rehman; Tahani Brarakah Alsaedi (Science and Technology - Recent Updates and Future Prospects Vol. 1, 2024)
The COVID-19 epidemic, which began on December 31, 2019, with the revelation of nonspecific pneumonia indications in Wuhan, China, swiftly became a significant outbreak, with great ramifications worldwide. The coronavirus epidemic (COVID-19) was growing quickly around the globe. The first acute atypical respiratory illness was reported in December 2019, in Wuhan, China. This quickly spread from Wuhan city to other locations. Deep learning (DL) algorithms are one of the greatest solutions for consistently and readily recognizing COVID-19. Previously, many researchers used state-of-the-art approaches for the classification of COVID-19. In this paper, we present a deep learning approach with the Efficient netB4 model, centered on transfer learning, for the classification of COVID-19. Transfer learning is a popular technique that uses pre-trained models that have been trained on the ImageNet database and employed on a new problem to increase generalization. We presented an in-depth training approach to extract the visual properties of COVID-19 in exchange for providing a medical assessment before infection testing. The proposed methodology is assessed on a publicly accessible X-ray imaging dataset. The experimentation work was conducted at the university using the Anaconda 3 software environment. Performance measurements were employed on the COVID-19 dataset to evaluate and verify our proposed approach. The proposed framework achieves an accuracy of 97%. Our model’s experimental findings demonstrate that it is extremely successful at identifying COVID-19 and that it may be supplied to health organizations as a precise, quick, and successful decision support system for COVID-19 identification. More data may be integrated into future work for improved outcomes, which would enhance the proposed framework even more.