Journal of Cybersecurity and Information Management JCIM 2690-6775 2769-7851 10.54216/JCIM https://www.americaspg.com/journals/show/2927 2019 2019 Department of Computer Science and Applications, Maharshi Dayanand University, Rohtak, Haryana, India Chhaya Chhaya Computer Science & Engineering with Artificial Intelligence and Data Science, Sagar Institute of Science and Technology, Gandhi Nagar Campus, Opposite International Airport, Bhopal (M.P.), 462036 Vasima Khan Assistant Professor, Department of CSE, Nitte Meenakshi Institute of Technology Ramya Srikanteswara Department of Computer Science and Applications, Maharshi Dayanand University, Rohtak, Haryana, India Nasib Singh Gill Department of Computer Science and Applications, Maharshi Dayanand University, Rohtak, Haryana, India Preeti Gulia Professor, School of Computing and Information Technology, REVA University, Bangalore, Karnataka, India Sindhu Menon Automatic detection of a medical disease is a need of the hour as it helps doctors diagnose diseases and provide fast medical reports. COVID-19 is a deadly disease for which an automated detection system may be helpful. This study proposes a unique hybrid deep learning model, COVIDet, based on CNN and the speeded-up robust features (SURF) extraction approach to diagnose COVID-19 using chest x-ray images. SURF is utilized in this work to extract features, and the output is then transferred to a 25-layer CNN for detection using the extracted features. This investigation employed 4623 COVID-19 positive X-ray pictures or 8055 total. The suggested hybrid model also contrasts with the study's VGG19, Resnet50, Inception, Xception, and traditional CNN models. The proposed model had a 98.01% accuracy, a 97.03% F1-score, a 98.65% sensitivity, a 99% precision, and a 95.65% specificity. The proposed model can be further improved when more datasets are available and can help doctors to diagnose patients quickly and efficiently. Using chest X-ray pictures, a secured web application is also developed to identify COVID-19. The user sends the application a chest X-ray image, and in return, it determines whether an individual is COVID-19 positive or not, cutting down on testing time. In Covid times, when people are standing in long queues and waiting for their turns, this application would greatly help. The application uses the pre-trained COVIDet model in the backend. 2024 2024 227 244 10.54216/JCIM.140116 https://www.americaspg.com/articleinfo/2/show/2927