COVID-19 Diagnosis From CT-images Using Transfer Learning

Abstract

In symptomatic patients, a positive COVID-19 test is critical for securing life-saving services such as ICU care and ventilator support; it may cause septic shock, septic pneumonia, respiratory failure, heart difficulties, liver issues, and even death. CAD systems help people in rural places and doctors in the early detection of COVID-19. A diagnostic and severity detection technique utilizing transfer learning and a backpropagation neural network has been developed with the aid of a computer for this purpose. This study aims to compare and analyze multiple deep learning-enhanced strategies for detecting COVID-19 in CT scan medical images. The COVID-19 CT scan binary classification challenge utilized two powerful pretrained CNN models: Inception ResNet V2 and ResNet50. To achieve higher accuracy in the diagnosis of COVID-19 using CT scan images, a new approach called Inception ResNet was employed, and it resulted in 97.3% accuracy and 97.38% specificity. Transfer learning techniques were employed to reduce the training time and get around the shortage of data. The proposed approaches outperformed more than other papers in the literature by 0.2%.

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