Role of Hybrid Deep Neural Networks (HDNNs), Computed Tomography, and Chest X-rays for the Detection of COVID-19

• Published in: International journal of environmental research and public health Vol. 18; no. 6; p. 3056
• Main Authors: Irfan, Muhammad, Iftikhar, Muhammad Aksam, Yasin, Sana, Draz, Umar, Ali, Tariq, Hussain, Shafiq, Bukhari, Sarah, Alwadie, Abdullah Saeed, Rahman, Saifur, Glowacz, Adam, Althobiani, Faisal
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 16.03.2021; MDPI
• Subjects: Accuracy; Algorithms; Automation; Classification; Coronaviruses; COVID-19; Datasets; Deep Learning; Epidemics; Health care; Humans; Infections; Neural networks; Neural Networks, Computer; Noise; Patients; Radiography, Thoracic; SARS-CoV-2; Severe acute respiratory syndrome coronavirus 2; Tomography; Tomography, X-Ray Computed; Wavelet transforms; X-Rays; COVID-19; computed tomography (CT-scan); long short-term memory (LSTM); hybrid deep neural network (HDNNs)
• ISSN: 1660-4601; 1661-7827; 1660-4601
• DOI: 10.3390/ijerph18063056

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.