Online diagnosis of COVID-19 from chest radiography images by using deep learning algorithms

Budak C., Mençik V., Varışlı O.

Neural Computing and Applications, vol.35, no.28, pp.20717-20734, 2023 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Volume: 35 Issue: 28
  • Publication Date: 2023
  • Doi Number: 10.1007/s00521-023-08867-5
  • Journal Name: Neural Computing and Applications
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Applied Science & Technology Source, Biotechnology Research Abstracts, Compendex, Computer & Applied Sciences, Index Islamicus, INSPEC, zbMATH
  • Page Numbers: pp.20717-20734
  • Keywords: COVID-19, Deep learning, Fine-tuning strategy, Online detection
  • Abdullah Gül University Affiliated: No


The COVID-19 outbreak, which has a devastating impact on the health and well-being of the global population, is a respiratory disease. It is vital to determine, isolate and treat people with the disease as soon as possible to fight against the COVID-19 pandemic. Even though the reverse transcription polymerase chain reaction (RT-PCR) test, the accuracy of which is about 63%, seems to be a good option for determining COVID-19, it is a disadvantage is that test kits are few, are difficult to obtain in remote rural areas and have low accuracy. Chest X-ray (CXR) has become essential for rapidly diagnosing the rapidly spreading COVID-19 disease worldwide, so it is urgent to develop an online system that will help specialists identify infected patients with CXR images. In this study developed a transfer learning-based diagnosis system for online diagnosis of COVID-19 patients using CXR images. Transfer learning-based deep learning models VGG16, VGG19, ResNet50, InceptionV3, Xception, MobileNet, DenseNet121 and DenseNet201 were used for the experimental studies. We explored the COVID-19 radiography database from Kaggle, which is open to the public, using image preprocessing techniques and data augmentation. The images captured by the various terminals are transferred to the web server in the created system. Similar to the ensemble learning approach, the percentage accuracy of the model with the highest prediction value among the eight deep learning models is displayed on the screen. The results show that the proposed online diagnosis system performs better than others with the highest accuracy, precision, recall and F1 values of 98%, 99%, 97% and 97%, respectively. The results show that deep learning models help to increase the efficiency of chest radiograph scanning and have promising potential in predicting COVID-19 cases. The online diagnostic system will be a helpful tool for radiologists as it diagnoses COVID-19 quickly and with high accuracy.