Detection of COVID-19 and Viral Pneumonia from Chest X-Ray Images with Deep Learning

Year 2023, , 89 - 100, 01.03.2023

Pınar Tüfekçi , Burak Gezici

https://doi.org/10.35414/akufemubid.1152432

Abstract

In today's conditions, although the pandemic has lost its effects and is in the process of ending, COVID-19 still shows its effects on people as mild. With the developments in image processing and Artificial Intelligence technologies, the correct detection of such viruses in the early stages will both help the healing process of the disease quickly by applying the right treatment, and will alleviate the burden on health systems. In this study, it has been tried to create a high-accuracy and reliable model that tries to distinguish COVID-19 and viral pneumonia diseases from chest X-ray images. For this purpose, a comprehensive modeling study has been carried out by applying the AlexNet and GoogleNet special architectures of convolutional neural networks, which are deep learning algorithms, directly to their original versions and to their improved versions with transfer learning. The data set used in the modeling process (COVID-19 Radiography Database) is a popular data set and an unbalanced data set with 3 classes and a different number of samples in each class. By applying data reduction and increase methods to this data set, 2 new balanced data sets containing equal number of samples in each class were created. By dividing the original dataset and newly created datasets into training and test datasets at a rate of 80-20, and also by cross validating 3, 5, and 10 times, model performances were measured and the model with the best performance was tried to be found. As a result, the best model was found with 99.90% accuracy, as the data set balanced by data augmentation method was divided according to cross validation 10 times and AlexNet architecture developed with transfer learning was applied.

Keywords

COVID-19, Viral pneumonia, Deep learning, AlexNet, GoogleNet

Derin Öğrenme ile Göğüs Röntgeni Görüntülerinden COVID-19 ve Viral Pnömoni Tespiti

Abstract

Günümüz şartlarında her ne kadar pandemi etkilerini kaybetmiş ve bitme sürecinde olsa da, COVID-19 halen etkilerini insanlar üzerinde hafif olarak göstermektedir. Yapay Zeka ve görüntü işleme teknolojilerindeki gelişmelerle birlikte, bu tür virüslerin erken aşamalarda doğru bir şekilde tespit edilmesi, hem doğru tedavinin uygulanarak hastalığın iyileşme sürecine hızlı bir şekilde yardımcı olacak hem de sağlık sistemlerinin üzerindeki yükü hafifletmiş olacaktır. Bu çalışmada, göğüs röntgeni görüntülerinden, COVID-19 ve viral pnömoni hastalıklarını ayırt etmeye çalışan, yüksek doğruluklu ve güvenilir bir model oluşturulmaya çalışılmıştır. Bu amaçla, derin öğrenme algoritmalarından olan Evrişimli Sinir Ağlarının AlexNet ve GoogleNet özel mimarilerinin, doğrudan orijinal halleri ve transfer öğrenmeyle geliştirilmiş halleri uygulanarak, geniş kapsamlı bir modelleme çalışması yapılmıştır. Modelleme sürecinde, kullanılan veri seti (COVID-19 Radiography Database) popüler bir veri seti olup, 3 sınıflı ve her sınıfta farklı sayıda örnek bulunduran dengesiz bir veri setidir. Bu veri setine, veri azaltma ve arttıma yöntemleri uygulanarak, her sınıfta eşit sayıda örnek içeren dengeli 2 yeni veri seti oluşturulmuştur. Orijinal veri seti ve yeni oluşturulan veri setleri, 80-20 oranında eğitim ve test veri setine bölünerek ve aynı zamanda 3, 5 ve 10 kez çapraz doğrulamaya göre bölünerek, model performansları ölçülmüştür ve en iyi performansa sahip model bulunmaya çalışılmıştır. Sonuç olarak, en iyi model, veri arttırma yöntemiyle dengeli hale getirilmiş veri setinin, 10 kez çapraz doğrulamaya göre bölünerek, transfer öğrenme ile geliştirilmiş AlexNet mimarisinin uygulandığı model olarak, % 99.90 doğruluk başarısı ile bulunmuştur.

Keywords

COVID-19, Viral pnömoni, Derin öğrenme, AlexNet, GoogleNet

References

• Adedigba, A.P., Adeshina, S.A., Aina, O.E. ve A. M. Albinu, A.M., 2021. Optimal hyperparameter selection of deep learning models for COVID-19 chest X-ray classification. Intelligence-Based Medicine, vol. 5, pp. 2666-5212.
• Ahuja, S., Panigrahi, B.K., Dey, N., Rajinikanth, V. ve Gandhi, T.K., 2021. Deep transfer learning-based automated detection of COVID-19 from lung CT scan slices. Applied Intelligence, vol. 51, pp. 571-585.
• Al-Bawi, A., Al-Kaabi, K., Jeryo, M. ve Al-Fatlawi, A., 2022. CCBlock: An Effective Use of Deep Learning for Automatic Diagnosis of COVID-19 Using X-Ray Images. Research on Biomedical Engineering, vol. 38, pp. 49-58.
• Alhudhaif, A., Polat, K. ve Karaman, O, 2021. Determination of COVID-19 Pneumonia based on generalized convolutional neural network model from chest X-ray images. Expert Systems with Applications, vol. 180, pp. 0957-4174.
• Apostolopoulos, I.D. ve Mpesiana, T.A., 2020. Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks. Physical and Engineering Sciences in Medicine, vol. 43, pp. 2662-4737.
• Chouhan, V., Singh, S.K., Khamparia, A., Gupta, D., Tiwari, P., Moreira, C., Damaseviclus, R. ve Albuquerque, V.H.C.D., 2020. A Novel Transfer Learning Based Approach for Pneumonia Detection in Chest X-Ray Images. Applied Sciences, vol. 10, pp. 2076-3417.
• Elshennawy, N.M. ve Ibrahim, D.M., 2020. Deep-Pneumonia Framework Using Deep Learning Models Based on Chest X-Ray Images. Diagnostics, vol. 10, pp. 1-16.
• Farooq, M. ve Hafeez, A., 2020. COVID-ResNet: A Deep Learning Framework for Screening of COVID-19 from Radiographs. arXiv eese arXiv:2003.14395.
• Kalaycı, T.E., 2018. Comparison of Machine Learning Techniques for Classification of Phishing Web Sites. Pamukkale University Journal of Engineering Sciences, vol. 24(5), pp. 870–878.
• Khan, A.I., Shah, J.L. ve Bhat, M.M., 2020. CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images. Computer Methods and Programs in Biomedicine, vol. 196, pp. 169-2607.
• Kim, T.K., Yi, P.H., Hager, G.D. ve Lin, C.T., 2019. Refining dataset curation methods for deep learning-based automated tuberculosis screening. Journal of Thoracic Disease, vol. 12, pp. 2077-6624.
• Konar, D., Panigrahi, B.K., Bhattacharyya, S. ve Dey, N., 2021. Auto-Diagnosis of COVID-19 using Lung CT Images With Semi-Supervised Shallow Learning Network. IEEE Access, vol. 9, pp. 28716-28728.
• Krizhevsky, A., Sutskever, I. ve Hinton, G., 2012. ImageNet Classification with Deep Convolutional Neural Networks. NIPS'12 Proceedings of the 25th International Conference on Neural Information Processing Systems, cilt 1, pp. 1097-1105.
• Lecun, Y., Bottou, L., Bengio, Y. ve Haffner, P., 1998. Gradient-based learning applied to document recognition. Proceeding of the IEEE, cilt 86, pp. 2278-2324.
• Loey, M., Smarandache, F. ve Khalifa, N.E.M, 2020. Within the Lack of Chest COVID-19 X-ray Dataset: A Novel detection Model Based on GAN and Deep Transfer Learning. Journal of Symmetry, vol. 12, pp. 2073-8994.
• Majeed, T., Rashid, R., Ali, D. ve Asaad, A., 2020. Covid-19 detection using CNN transfer learning from X-ray Images. Physical and Engineering Sciences in Medicine, vol. 43, pp. 1289–1303
• Minaee, S., Kafieh, R., Sonka, M., Yazdani, S. ve Soufi, G.J., 2020. Deep-COVID: Predicting COVID-19 from chest X-ray images using deep transfer learning. Medical Image Analysis, vol. 65, pp. 1361-8415.
• Nour, M., Cömert, Z. ve Polat, K., 2020. A Novel Medical Diagnosis model for COVID-19 infection detection based on Deep Features and Bayesian Optimization. Applied Soft Computing, vol. 97, pp. 1568-4946.
• Ozturk, T., Talo, M., Yildirim, E.A., Baloglu, U.B., Yildirim, O. ve Acharya, U.R., 2020. Automated detection of COVID-19 cases using deep neural networks with X-ray images. Computers in Biology and Medicine, vol. 121, pp. 0010-4825.
• Panwar, H., Gupta, P.K., Siddiqui, M.K., Menendez, R.M. ve Singh, V., 2020. Application of deep learning for fast detection of COVID-19 in X-Rays using nCOVnet. Chaos, Solitions & Fractals, vol. 138, pp. 0960-0779.
• Pham, T.D., 2020. Classification of COVID-19 chest X-rays with deep learning: new models or fine tuning. Health Information Science and Systems, vol. 9, pp. 2047-2501.
• Rahman, T., Chowdhury, M.E.H., Khandakar, A., Islam, K.R., Islam, K.F., Mahbub, Z.B., Kadir, M.A. ve Kashem, S., 2020. Transfer Learning with Deep Convolutional Neural Network (CNN) for Pneumonia Detection using Chest X-ray. Applied Sciences, vol. 10, pp. 2076-3417.
• Rajpurkar, P., Irvin, J., Zhu, K., Yang, B., Mehta, H., Duan, T., Ding, D., Bagul, A., Ball, R.L., Langlotz, C., Shpanskaya, K., Lungren, M.P. ve Ng, A.Y., 2017. CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning. arXiv cs arXiv:1711.05225.
• Ucar, F. ve Korkmaz, D., 2020. COVIDiagnosis-Net: Deep Bayes-SqueezeNet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images. Med Hypotheses, vol. 140, pp. 1532-2777.
• Umer, M., Ashraf, I., Ullah, S., Mahmood, A. ve G. S. Choi, G.S., 2022. COVINet: A Convolutional neural network approach for predicting COVID-19 from chest X-ray images. Journal of Ambient Intelligence and Humanized Computing, vol. 13, pp. 535–547.
• Shorten, C. ve Khoshgoftaar, T.M., 2019. A survey on Image Data Augmentation for Deep Learning. Journal of Big Data, vol. 6, pp. 2196-1115.
• Szegedy, C., et al., 2015. Going deeper with convolutions. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1-9.
• Toraman, S., Alakus, T.B. ve I. Türkoğlu, I., 2020. Convolutional capsnet: A novel artificial neural network approach to detect COVID-19 disease from X-ray images using capsule networks. Chaos, Solitions & Fractals, vol. 140.
• Vaid, S., Kalantar, R. ve Bhandari, M., 2020. Deep learning COVID-19 detection bias: accuracy through artificial intelligence. International Orthopaedics, vol. 44, pp. 1432-5195.
• Yi, P.H., Kim, T.K. ve Lin, C.T., 2020. Generalizability of Deep Learning Tuberculosis Classifier to COVID-19 Chest Radiographs: New Tricks for an Old Algorithm? Journal of Thoracic Imaging, vol. 35, pp. 102-104.
• https://www.kaggle.com/tawsifurrahman/covid19-radiography-database (15.03.2021)