Classification of Pneumonia Using Pre-Trained Deep Networks with Chest X-Ray Images

Abstract

Pneumonia is a lung infection that can be caused by bacteria, viruses, or fungi. The infection causes the lungs to become inflamed and filled with fluid or pus. It can be a serious and life-threatening disease. Many people die every year due to pneumonia worldwide. Early detection and treatment of pneumonia can significantly reduce mortality. For this reason, this research is to propose a method based on pre-trained deep network models using x-ray images to detect pneumonia. Various pre-trained Convolutional Neural Networks were used as feature extractors to classify chest x-ray images into two classes without pneumonia and pneumonia. AlexNet, VGG16, ResNet (ResNet18, ResNet50, ResNet101) models are preferred as pre-trained deep network models. The hybrid feature vector is obtained by combining the features obtained from these models. As the classifier, Support Vector Machines (SVM) and Softmax in the last layer of deep networks are used. Experiments are carried out on the data set commonly used in the literature. The highest classification success is obtained from the hybrid feature vector as 98.32%.

Keywords

• Pneumonia
• Convolutional Neural Networks
• AlexNet
• ResNet
• VGG16

ÖNCEDEN EĞİTİLMİŞ DERİN AĞLAR İLE GÖĞÜS RÖNTGENİ GÖRÜNTÜLERİ KULLANARAK PNÖMONİ SINIFLANDIRILMASI

Abstract

Pnömoni, bakterilerin, virüslerin veya mantarların neden olabileceği bir akciğer enfeksiyonudur. Enfeksiyon, akciğerlerin hava keselerinin iltihaplanmasına ve sıvı veya irin ile dolmasına neden olur. Ciddi ve hayatı tehdit eden bir hastalık olabilir. Dünya genelinde her yıl pnömoni nedeniyle çok sayıda kişi ölmektedir. Pnömoninin erken tespiti ve tedavisi, ölüm oranlarını önemli ölçüde azaltabilmektedir. Bu nedenle, bu araştırmada pnömoniyi tespit etmek için röntgen görüntüleri kullanarak önceden eğitilmiş derin öğrenme modellerine dayanan yöntem önerilmektir. Göğüs röntgen görüntülerini pnömoni ve pnömoni olmayan iki sınıfta sınıflandırmak için çeşitli önceden eğitilmiş Evrişimsel Sinir Ağları özellik çıkarıcı olarak kullanılmıştır. Önceden eğitilmiş derin öğrenme modelleri olarak AlexNet, VGG16, ResNet (ResNet18, ResNet50, ResNet101) modelleri tercih edilmiştir. Bu modellerden elde edilen özellikler birleştirilerek hibrit özellik vektörü elde edilmiştir. Sınıflandırıcı olarak Destek Vektör Makineleri (DVM) ve derin öğrenme modellerinin son katmanında bulunan Softmax kullanılmıştır. Deneyler literatürde yaygın kullanılan veri seti üzerinde yapılmıştır. En yüksek sınıflandırma başarısı %98,32 olarak hibrit özellik vektöründen elde edilmiştir.

Keywords

• Pnömoni
• Evrişimsel sinir ağlar
• AlexNet
• ResNet
• VGG16

References

1. Anthimopoulos, M., Christodoulidis, S., Ebner, L., Christe, A., & Mougiakakou, S. (2016). Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network. IEEE Transactions on Medical Imaging, 35(5), 1207–1216. https://doi.org/10.1109/tmi.2016.2535865
2. Aydoğdu, M., Ozyilmaz, E., Aksoy, H., Gürsel, G., & Ekim, N. (2010). Mortality prediction in community- acquired pneumonia requiring mechanical ventilation; values of pneumonia and intensive care unit severity scores. Tüberküloz ve Toraks, 58, 25–34.
3. Davies, H. D., Wang, E. E., Manson, D., Babyn, P., & Shuckett, B. (1996). Reliability of the chest radiograph in the diagnosis of lower respiratory infections in young children. The Pediatric Infectious Disease Journal, 15(7), 600–604. https://doi.org/10.1097/00006454-199607000-00008
4. Er, M. B., & Aydilek, I. B. (2019). Music Emotion Recognition by Using Chroma Spectrogram and Deep Visual Features. International Journal of Computational Intelligence Systems, 12(2), 1622–1634. https://doi.org/https://doi.org/10.2991/ijcis.d.191216.001
5. Guan, Q., Huang, Y., Zhong, Z., Zheng, Z., Zheng, L., & Yang, Y. (2019). Thorax Disease Classification with Attention Guided Convolutional Neural Network. Pattern Recognition Letters, 131. https://doi.org/10.1016/j.patrec.2019.11.040
6. Jain, R., Nagrath, P., Kataria, G., Sirish Kaushik, V., & Jude Hemanth, D. (2020). Pneumonia detection in chest X-ray images using convolutional neural networks and transfer learning. Measurement, 165, 108046. https://doi.org/10.1016/j.measurement.2020.108046
7. Jakhar, K., & Hooda, N. (2018). Big Data Deep Learning Framework using Keras: A Case Study of Pneumonia Prediction. In 2018 4th International Conference on Computing Communication and Automation (ICCCA). IEEE. https://doi.org/10.1109/ccaa.2018.8777571
8. Kabir, E., Siuly, & Zhang, Y. (2016). Epileptic seizure detection from EEG signals using logistic model trees. Brain Informatics, 3(2), 93–100. https://doi.org/10.1007/s40708-015-0030-2

9. Kallianos, K., Mongan, J., Antani, S., Henry, T., Taylor, A., Abuya, J., & Kohli, M. (2019). How far have we come? Artificial intelligence for chest radiograph interpretation. Clinical Radiology, 74(5), 338– 345. https://doi.org/10.1016/j.crad.2018.12.015
10. Kumar Acharya, A., & Satapathy, R. (2020). A Deep Learning Based Approach towards the Automatic Diagnosis of Pneumonia from Chest Radio-Graphs. Biomedical and Pharmacology Journal, 13(1), 449–455. https://doi.org/10.13005/bpj/1905
11. Mooney, P. (2020). Chest X-Ray Images (Pneumonia). https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia
12. Rahman, T., Chowdhury, M. E. H., Khandakar, A., Islam, K. R., Islam, K. F., Mahbub, Z. B., Kadir, M. A., & Kashem, S. (2020). Transfer Learning with Deep Convolutional Neural Network (CNN) for Pneumonia Detection Using Chest X-ray. Applied Sciences, 10(9), 3233. https://doi.org/10.3390/app10093233
13. Rajaraman, S., Candemir, S., Kim, I., Thoma, G., & Antani, S. (2018). Visualization and Interpretation of Convolutional Neural Network Predictions in Detecting Pneumonia in Pediatric Chest Radiographs. Applied Sciences, 8, 1715. https://doi.org/10.3390/app8101715
14. Rajpurkar, P., Irvin, J., Zhu, K., Yang, B., Mehta, H., Duan, T., Ding, D., Bagul, A., Langlotz, C., Shpanskaya, K., Lungren, M., & Ng, A. (2017). CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning.
15. Rubin, J., Sanghavi, D., Zhao, C., Lee, K., Qadir, A., & Xu-Wilson, M. (2018). Large Scale Automated Reading of Frontal and Lateral Chest X-Rays using Dual Convolutional Neural Networks.
16. Rudan, I., Tomaskovic, L., Boschi-Pinto, C., & Campbell, H. (2004). Global estimate of the incidence of clinical pneumonia among children under five years of age. Bulletin of the World Health Organization, 82(12), 895–903. Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A., & Li, F. F. (2014). ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision, 115. https://doi.org/10.1007/s11263-015-0816-y
17. Salamon, J., & Bello, J. P. (2017). Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification. IEEE Signal Processing Letters, 24(3), 279–283. https://doi.org/10.1109/LSP.2017.2657381
18. Saraiva, A., Santos, D., Costa, N., Sousa, J., Ferreira, N., Valente, A., & Soares, S. (2019). Models of Learning to Classify X-ray Images for the Detection of Pneumonia using Neural Networks. In Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies. SCITEPRESS - Science and Technology Publications. https://doi.org/10.5220/0007346600760083
19. Sirazitdinov, I., Kholiavchenko, M., Mustafaev, T., Yixuan, Y., Kuleev, R., & Ibragimov, B. (2019). Deep neural network ensemble for pneumonia localization from a large-scale chest x-ray database. Computers & Electrical Engineering, 78, 388–399. https://doi.org/10.1016/j.compeleceng.2019.08.004
20. Stephen, O., Sain, M., Maduh, U. J., & Jeong, D.-U. (2019). An Efficient Deep Learning Approach to Pneumonia Classification in Healthcare. Journal of Healthcare Engineering, 2019, 4180949. https://doi.org/10.1155/2019/4180949
21. Varshni, D., Thakral, K., Agarwal, L., Nijhawan, R., & Mittal, A. (2019). Pneumonia Detection Using CNN based Feature Extraction. In 2019 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT). IEEE. https://doi.org/10.1109/icecct.2019.8869364
22. WHO. (2001). Standardization of interpretation of chest radiographs for the diagnosis of pneumonia in children.
23. Zisserman, K. S. and A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. https://arxiv.org/abs/1409.1556