Recognition of Covid-19 Over CT Images Using CNN and Transfer Learning - Ravi Kanth Motupalli, Sunanda Nalajala, Praveen Tumuluru, P. Venu Madhav, T. Venkata Naga Jayudu, Konda Raveendra Kumar (International Journal of Intelligent Systems and Applications in Engineering, 2024)
The emergence of COVID-19, a novel corona virus pneumonia, in 2019 has a significant impact on the growth of the global economy and the lives of individuals. Deep learning networks have been developed as a new, widely utilised image processing technique they have been employed as an unique detection approach in clinical practise to retrieve medical info from CT pictures. Yet, because to the medical features of Corona virus Computed tomography scans. As a result, Using the current deep learning method, diagnosis is tough. The global feature extraction advantage of the Transformer module and CNNs module is provided, and the concurrent trans model (TransCNN Net) based on Transformer is employed to fully use the local extraction of features capabilities of the CNN Model.. This is done in line with the COVID-19's medical characteristics on the CT scans. By extracting features from two branches and fusing them in opposite directions, the cross-fusion notion results in a bi-directional feature fusion structure. .A feature fusion module then joins the parallel branch structures to create a model that can identify scale-dependent properties. The classification accuracy of covid data is 96.7, which is greater than Transform Network (Diet-B). The outcomes show increased accuracy. A new approach for diagnosing COVID-19 is also provided by this model, and by it encourages the growth of instantaneous identification pulmonary issues brought on by a corona infection by integrating machine learning with medical imaging. This approach enables an accurate and speedy diagnosis, saving precious lives.