COVID-19 syndrome has extensively escalated worldwide with the induction of the year 2020 and has resulted in the illness of millions of people. COVID-19 patients bear an elevated risk once the symptoms deteriorate. Hence, early recognition of diseased patients can facilitate early intervention and avoid disease succession. This article intends to develop a hybrid deep neural networks (HDNNs), using computed tomography (CT) and X-ray imaging, to predict the risk of the onset of disease in patients suffering from COVID-19. To be precise, the subjects were classified into 3 categories namely normal, Pneumonia, and COVID-19. Initially, the CT and chest X-ray images, denoted as ‘hybrid images’ (with resolution 1080 × 1080) were collected from different sources, including GitHub, COVID-19 radiography database, Kaggle, COVID-19 image data collection, and Actual Med COVID-19 Chest X-ray Dataset, which are open source and publicly available data repositories. The 80% hybrid images were used to train the hybrid deep neural network model and the remaining 20% were used for the testing purpose. The capability and prediction accuracy of the HDNNs were calculated using the confusion matrix. The hybrid deep neural network showed a 99% classification accuracy on the test set data.
【저자키워드】 COVID-19, long short-term memory (LSTM), hybrid deep neural network (HDNNs), computed tomography (CT-scan), 【초록키워드】 Pneumonia, risk, Intervention, Symptom, database, Computed tomography, X-ray, Accuracy, Patient, chest X-ray, disease, predict, COVID-19 patients, Data collection, Chest X-ray images, COVID-19 patient, open, 1080, onset of disease, confusion matrix, network model, Confusion, available data, subject, Github, syndrome, Kaggle, actual, collected, develop, elevated, facilitate, calculated, were used, category, deteriorate, Initially, 【제목키워드】 detection, Computed tomography, network, hybrid, role, deep, Neural,