A Computer-Assisted Diagnosis Tool for Classifying COVID-19 based on Chest X-Ray Images

Year 2021, , 438 - 445, 30.08.2021

Fatma Hilal Yağın , Emek Güldoğan , Hasan Ucuzal , Cemil Çolak

https://doi.org/10.18521/ktd.947192

Cited By: 7

Abstract

Objective: Since COVID-19 is a worldwide pandemic, COVID-19 detection using a convolutional neural network (CNN) has been an extraordinary research technique. In the reported studies, many models that can predict COVID-19 based on deep learning methods using various medical images have been created; however, clinical decision support systems have been limited. The aim of this study is to develop a successful deep learning model based on X-ray images and a computer-assisted, fast, free and web-based diagnostic tool for accurate detection of COVID-19.
Method: In this study a 15-layer CNN model was used to detect COVID-19 using X-ray images, which outperformed many previously published CNN models in terms of classification. The model performance is evaluated according to Accuracy, Matthews Correlation Coefficient (MCC), F1 Score, Specificity, Sensitivity (Recall), Youden’s Index, Precision (Positive Predictive Value: PPV), Negative Predictive Value (NPV), and Confusion Matrix (Classification matrix). In the second phase of the study, the computer-aided diagnostic tool for COVID-19 disease was developed using Python Flask library, JavaScript and Html codes.
Results: The model to diagnose COVID-19 has an average accuracy of 98.68 % in the training set and 96.98 % in the testing set. Among the evaluation metrics, the minimum value is 93.4 % for MCC and Youden’s index, and the maximum value is 97.8 for sensitivity and NPV. A higher sensitivity value means a lower false negative (FN) value, and a low FN value is an encouraging outcome for COVID-19 cases. This conclusion is crucial because minimizing the overlooked cases of COVID-19 (false negatives) is one of the main goals of this research.
Conclusion: In this period when COVID-19 is spreading rapidly around the world, it is thought that the free and web-based COVID-19 X-Ray clinical decision support tool can be a very effective and fast diagnostic tool. The computer-aided system can assist physicians and radiologists in making clinical decisions about the disease, as well as provide support in diagnosis, follow-up, and prognosis. The developed computer-assisted diagnosis tool can be publicly accessed at http://biostatapps.inonu.edu.tr/CSYX/.

Keywords

Convolutional Neural Network, COVID-19, Image Processing, Deep Learning, Computer-Aided Diagnostic Systems

Supporting Institution

İNÖNÜ ÜNİVERSİTESİ BİLİMSEL ARAŞTIRMA PROJELERİ KOORDİNASYON BİRİMİ

Project Number

TYL-2021-2539

Thanks

This study was supported by Inonu University Scientific Research Projects Coordination Unit within the scope of TYL-2021-2539 numbered research project.

Göğüs Röntgeni Görüntülerine Dayalı COVID-19'u Sınıflandırmak için Bilgisayar Destekli Bir Tanı Aracı

Abstract

Amaç: COVID-19 dünya çapında bir salgın olduğu için, evrişimli sinir ağı (CNN) kullanılarak COVID-19 tespiti olağanüstü bir araştırma tekniği olmuştur. Bildirilen çalışmalarda, çeşitli tıbbi görüntüler kullanılarak derin öğrenme yöntemlerine dayalı olarak COVID-19'u tahmin edebilen birçok model oluşturulmuş; ancak, klinik karar destek sistemleri sınırlı kalmıştır. Bu çalışmanın amacı, X- ışını görüntülerine dayalı başarılı bir derin öğrenme modeli ve COVID-19'un doğru tespiti için bilgisayar destekli, hızlı, ücretsiz ve web tabanlı bir tanı aracı geliştirmektir.
Gereç ve Yöntem: Bu çalışmada, sınıflandırma açısından daha önce yayınlanmış birçok CNN modelinden daha iyi performans gösteren X-ışını görüntüleri kullanılarak COVID-19'u tespit etmek için 15 katmanlı bir CNN modeli kullanıldı. Model performansı Doğruluk, Matthews Korelasyon Katsayısı (MCC), F1 Skoru, Seçiçilik, Duyarlılık, Youden Endeksi, Kesinlik (Pozitif Tahmin Değeri: PPV), Negatif Tahmin Değeri (NPV) ve Karışıklık Matrisine (Sınıflandırma matrisi) dayalı olarak değerlendirildi. Çalışmanın ikinci aşamasında Python Flask kütüphanesi, JavaScript ve Html kodları kullanılarak COVID-19 için bilgisayar destekli tanı aracı geliştirildi.
Bulgular: COVID-19 tanısına yönelik model, eğitim setinde ortalama %98.68 ve test setinde %96.98 doğruluk oranına sahiptir. Değerlendirme ölçütlerinden minimum değerler MCC ve Youden Endeksi için %93.4, maksimum değer ise duyarlılık ve NPV ölçütlerinde % 97.8 olarak elde edilmiştir. Daha yüksek bir duyarlılık değeri, daha düşük bir yanlış negatif (FN) değeri anlamına gelir ve düşük bir FN değeri, COVID-19 vakaları için cesaret verici bir sonuçtur. Bu sonuç çok önemlidir, çünkü gözden kaçan COVID-19 vakalarını (yanlış negatifler) en aza indirmek bu araştırmanın ana hedeflerinden biridir.
Sonuç: COVID-19'un dünya çapında hızla yayıldığı bu dönemde, ücretsiz ve web tabanlı COVID-19 X-Ray klinik karar destek aracının oldukça etkili ve hızlı bir tanı aracı olabileceği düşünülmektedir. Bilgisayar destekli sistem, doktorlara ve radyologlara hastalık hakkında klinik kararlar vermede yardımcı olabileceği gibi, teşhis, takip ve prognoz konusunda da destek sağlayabilir. Geliştirilen bilgisayar destekli tanı aracına http://biostatapps.inonu.edu.tr/CSYX/ adresinden genel erişim sağlanabilmektedir.

Keywords

Evrişimli sinir ağı, COVID-19, Görüntü işleme, Derin öğrenme, Bilgisayar destekli tanı sistemleri

Project Number

TYL-2021-2539

References

• 1. Wu F, Zhao S, Yu B, Chen Y-M, Wang W, Song Z-G, et al. A new coronavirus associated with human respiratory disease in China. Nature. 2020;579(7798):2652.
• 2. Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. The lancet. 2020;395(10223):497-506.
• 3. Wu Z, McGoogan JM. Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19) outbreak in China: summary of a report of 72 314 cases from the Chinese Center for Disease Control and Prevention. Jama. 2020;323(13):1239-42.
• 4. Timeline-COVID W, Organization WH. Apr 27. URL: https://www who int/news-room/detail/27-04-2020-who-timeline---covid-19 [accessed 2020-04-27]. 2020.
• 5. Kong W, Agarwal PP. Chest imaging appearance of COVID-19 infection. Radiology: Cardiothoracic Imaging. 2020;2(1):e200028.
• 6. van Dorp L, Acman M, Richard D, Shaw LP, Ford CE, Ormond L, et al. Emergence of genomic diversity and recurrent mutations in SARS-CoV-2. Infection, Genetics and Evolution. 2020;83:104351.
• 7. Mishal A, Saravanan R, Atchitha SS, Santhiya K, Rithika M, Menaka SS, et al. A Review of Corona Virus Disease-2019. History. 2020;4:07.
• 8. Zu ZY, Jiang MD, Xu PP, Chen W, Ni QQ, Lu GM, et al. Coronavirus disease 2019 (COVID-19): a perspective from China. Radiology. 2020;296(2):E15-E25.
• 9. Kanne JP, Little BP, Chung JH, Elicker BM, Ketai LH. Essentials for radiologists on COVID-19: an update—radiology scientific expert panel. Radiological Society of North America; 2020.
• 10. Lee EY, Ng M-Y, Khong P-L. COVID-19 pneumonia: what has CT taught us? The Lancet Infectious Diseases. 2020;20(4):384-5.
• 11. Bernheim A, Mei X, Huang M, Yang Y, Fayad ZA, Zhang N, et al. Chest CT findings in coronavirus disease-19 (COVID-19): relationship to duration of infection. Radiology. 2020:200463.
• 12. Long C, Xu H, Shen Q, Zhang X, Fan B, Wang C, et al. Diagnosis of the Coronavirus disease (COVID-19): rRT-PCR or CT? European journal of radiology. 2020;126:108961.
• 13. Li Y, Xia L. Coronavirus disease 2019 (COVID-19): role of chest CT in diagnosis and management. American Journal of Roentgenology. 2020;214(6):1280-6.
• 14. Shi H, Han X, Jiang N, Cao Y, Alwalid O, Gu J, et al. Radiological findings from 81 patients with COVID-19 pneumonia in Wuhan, China: a descriptive study. The Lancet infectious diseases. 2020;20(4):425-34.
• 15. Zhao W, Zhong Z, Xie X, Yu Q, Liu J. Relation between chest CT findings and clinical conditions of coronavirus disease (COVID-19) pneumonia: a multicenter study. American Journal of Roentgenology. 2020;214(5):1072-7.
• 16. Zou L, Zheng J, Miao C, Mckeown MJ, Wang ZJ. 3D CNN based automatic diagnosis of attention deficit hyperactivity disorder using functional and structural MRI. IEEE Access. 2017;5:23626-36.
• 17. Liu C, Cao Y, Alcantara M, Liu B, Brunette M, Peinado J, et al., editors. TX-CNN: Detecting tuberculosis in chest X-ray images using convolutional neural network. 2017 IEEE international conference on image processing (ICIP); 2017: IEEE.
• 18. Zhao X, Liu L, Qi S, Teng Y, Li J, Qian W. Agile convolutional neural network for pulmonary nodule classification using CT images. International journal of computer assisted radiology and surgery. 2018;13(4):585-95.
• 19. Liu J, Li W, Zhao N, Cao K, Yin Y, Song Q, et al., editors. Integrate domain knowledge in training CNN for ultrasonography breast cancer diagnosis. International Conference on Medical Image Computing and Computer-Assisted Intervention; 2018: Springer.
• 20. Kesim E, Dokur Z, Olmez T, editors. X-ray chest image classification by a small-sized convolutional neural network. 2019 scientific meeting on electrical-electronics & biomedical engineering and computer science (EBBT); 2019: IEEE.
• 21. Apostolopoulos ID, Mpesiana TA. Covid-19: automatic detection from x-ray images utilizing transfer learning with convolutional neural networks. Physical and Engineering Sciences in Medicine. 2020;43(2):635-40.
• 22. Wang L, Lin ZQ, Wong A. Covid-net: A tailored deep convolutional neural network design for detection of covid-19 cases from chest x-ray images. Scientific Reports. 2020;10(1):1-12.
• 23. Alqudah AM, Qazan S. Augmented COVID-19 X-ray images dataset. Mendeley Data. 2020;4.
• 24. O'Shea K, Nash R. An introduction to convolutional neural networks. arXiv preprint arXiv:151108458. 2015.
• 25. Li Y, Hao Z, Lei H. Survey of convolutional neural network. Journal of Computer Applications. 2016;36(9):2508-15.
• 26. Sanner MF. Python: a programming language for software integration and development. J Mol Graph Model. 1999;17(1):57-61.
• 27. Baxter G, Frean M, Noble J, Rickerby M, Smith H, Visser M, et al., editors. Understanding the shape of Java software. Proceedings of the 21st annual ACM SIGPLAN conference on Object-oriented programming systems, languages, and applications; 2006.
• 28. Raggett D, Le Hors A, Jacobs I. HTML 4.01 Specification. W3C recommendation. 1999;24.
• 29. Alqudah AM, Qazan S, Alquran H, Qasmieh IA, Alqudah A. COVID-2019 detection using X-ray images and artificial intelligence hybrid systems. Biomedical Signal and Image Analysis and Project; Biomedical Signal and Image Analysis and Machine Learning Lab: Boca Raton, FL, USA. 2019.
• 30. Ahmad F, Farooq A, Ghani MU. Deep Ensemble Model for Classification of Novel Coronavirus in Chest X-Ray Images. Computational Intelligence and Neuroscience. 2021;2021.
• 31. Ismael AM, Şengür A. Deep learning approaches for COVID-19 detection based on chest X-ray images. Expert Systems with Applications. 2021;164:114054.
• 32. Abbas A, Abdelsamea MM, Gaber MM. Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network. Applied Intelligence. 2021;51(2):854-64.
• 33. de Sousa PM, Carneiro PC, Oliveira MM, Pereira GM, da Costa Junior CA, de Moura LV, et al. COVID-19 classification in X-ray chest images using a new convolutional neural network: CNN-COVID. Research on Biomedical Engineering. 2021:1-11.