LAUSR

Background: The increasing number of COVID-19 patients around the world and the limited number of detection kits pose a challenge in determining the presence of the disease. Imaging modalities such as X-rays are commonly used because they are readily available and cost-effective. Deep learning has proved to be an excellent tool because of the abundance of online medical images in various medical modalities, such as X-Ray, computerized tomography (CT) Scan, and magnetic resonance imaging (MRI). A large number of medical research projects have been proposed and launched since early 2020 due to the overwhelming use of deep learning techniques in medical imaging. Methods: We have used fuzzy logic and deep learning to determine if chest X-ray images belong to people who have pneumonia related to COVID-19 and people who have interstitial pneumonias that aren't related to COVID-19. Results: In comparison to the current literature, the proposed transfer learning approach is more successful. It is possible to classify covid, viral, and bacterial pneumonia or a healthy patient using ResNet 18 Architecture's four-class classifiers. The proposed method achieved a 97% classification accuracy, 96% precision, and 98% recall in the case of COVID-19 detection using chest X-ray images, which demonstrates the importance of deep learning in medical image diagnosis.  Furthermore, the results demonstrate that the proposed technique has the maximum sensitivity rate, with 97.1% ratio. Finally, with a 97.47% F1-score rate, the proposed strategy yields the highest value when compared to the others. Conclusions: DeepLearning techniques and fuzzy features resulted in an improved classification ability, with an accuracy rate of up to 97.7% using ResNet 18, which is a better value when compared to the remaining techniques. Classification of COVID-19 scans and other pneumonia cases have been done successfully by demonstrating the potential for applying such deep learning techniques in the near future.