Göğüs röntgenlerinde pnömoniyi tespit etmek için derin öğrenme yaklaşımı

Year 2021, Issue: 28, 562 - 567, 30.11.2021

Muhammet Emin Şahin Hasan Ulutaş Esra Yüce

https://doi.org/10.31590/ejosat.1009434

Cited By: 2

Abstract

Pnömoni her yıl çok sayıda çocuğun ölümüne neden olmakta ve dünya nüfusunun belli bir oranını oluşturmaktadır. Göğüs röntgenleri öncelikle bu hastalığı teşhis etmek için kullanılır, ancak eğitimli bir radyolog için bile göğüs röntgenlerini yorumlamak kolay değildir. Bu çalışmada, radyologlara karar verme süreçlerinde yardımcı olmak için dijital göğüs röntgeni görüntüleri üzerinde eğitilmiş bir pnömoni tespiti modeli sunulmaktadır. Çalışma, Phyton platformunda son zamanlarda yaygın olarak tercih edilen derin öğrenme modelleri kullanılarak gerçekleştirilmiştir. Bu çalışmada, dört farklı CNN modeli ile pnömoni sınıflandırması için bir derin öğrenme çerçevesi önerilmiştir. Bunlardan üçü önceden eğitilmiş modeller, MobileNet, ResNet ve AlexNet, diğeri ise önerilen CNN modelidir. Bu modeller performanslarına göre birbirleriyle karşılaştırılarak değerlendirilmektedir. Önerilen derin öğrenme çerçevesinin deneysel performansı, kesinlik, duyarlılık ve F1-puanı temelinde değerlendirilir. Modeller sırasıyla %93, %97, %97 ve %86 doğruluk değerlerine ulaşmıştır. Önerilen ResNet modelinin diğerlerine kıyasla en yüksek sonuçları elde ettiği açıktır.

Keywords

ANN, Derin Öğrenme, Python, Zatürre, X-Ray

A deep learning approach for detecting pneumonia in chest X-rays

Abstract

Pneumonia causes the death of many children every year and constitutes a certain proportion of the world population. Chest X-rays are primarily used to diagnose this disease, but even for a trained radiologist, chest X-rays are not easy to interpret. In this study, a model for pneumonia detection trained on digital chest X-ray images is presented to assist radiologists in their decision-making processes. The study is carried out on the Phyton platform by using deep learning models, which have been widely preferred recently. In this study, a deep learning framework for pneumonia classification with four different CNN models is proposed. Three of them are pre-trained models, MobileNet, ResNet and AlexNet and the other is the recommended CNN Model. These models are evaluated by comparing them with each other according to their performance. The experimental performance of the proposed deep learning framework is evaluated on the basis of precision, recall and f1-score. The models achieved accuracy values of 93%, 97%, 97% and 86%, respectively. It is clear that the proposed ResNet model achieves the highest results compared to the others.

Keywords

CNN, Deep Learning, Python, Pneumonia, X-ray

References

• Akter, S., & Shamsuzzaman, J. F. (2015). Community acquired pneumonia. Int J Respir Pulm Med, 2, 2.
• McLuckie, A. (Ed.). (2009). Respiratory disease and its management. Springer Science & Business Media.
• Pommerville, J. C. (2012). Alcamo's Fundamentals of Microbiology: Body systems edition. Jones & Bartlett Publishers.
• Summah, H., & Qu, J. M. (2009). Biomarkers: a definite plus in pneumonia. Mediators of inflammation, 2009. D. Berliner, N. Schneider, T. Welte, and J. Bauersachs, “The differential diagnosis of dyspnoea,” Dtsch. Arztebl. Int., vol. 113, no. 49, pp. 834–844, 2016.
• He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).
• Ronneberger, O., Fischer, P., & Brox, T. (2015, October). U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention (pp. 234-241). Springer, Cham.
• Ezzy, H., Charter, M., Bonfante, A., & Brook, A. (2021). How the Small Object Detection via Machine Learning and UAS-Based Remote-Sensing Imagery Can Support the Achievement of SDG2: A Case Study of Vole Burrows. Remote Sensing, 13(16), 3191.
• Ragab, D. A., Sharkas, M., Marshall, S., & Ren, J. (2019). Breast cancer detection using deep convolutional neural networks and support vector machines. PeerJ, 7, e6201.
• Pereira, S., Pinto, A., Alves, V., & Silva, C. A. (2016). Brain tumor segmentation using convolutional neural networks in MRI images. IEEE transactions on medical imaging, 35(5), 1240-1251.
• Öztürk, Ş., & Özkaya, U. (2020). Skin lesion segmentation with improved convolutional neural network. Journal of digital imaging, 33(4), 958-970.
• Ayan, E., & Ünver, H. M. (2018, April). Data augmentation importance for classification of skin lesions via deep learning. In 2018 Electric Electronics, Computer Science, Biomedical Engineerings' Meeting (EBBT) (pp. 1-4). IEEE.
• Kieu, P. N., Tran, H. S., Le, T. H., Le, T., & Nguyen, T. T. (2018, November). Applying Multi-CNNs model for detecting abnormal problem on chest x-ray images. In 2018 10th International Conference on Knowledge and Systems Engineering (KSE) (pp. 300-305). IEEE.
• Baltruschat, I. M., Nickisch, H., Grass, M., Knopp, T., & Saalbach, A. (2019). Comparison of deep learning approaches for multi-label chest X-ray classification. Scientific reports, 9(1), 1-10.
• O’Quinn, W., Haddad, R. J., & Moore, D. L. (2019, January). Pneumonia radiograph diagnosis utilizing deep learning network. In 2019 IEEE 2nd International Conference on Electronic Information and Communication Technology (ICEICT) (pp. 763-767). IEEE.
• Wang, H., Jia, H., Lu, L., & Xia, Y. (2019). Thorax-net: an attention regularized deep neural network for classification of thoracic diseases on chest radiography. IEEE journal of biomedical and health informatics, 24(2), 475-485.
• Hammoudi, K., Benhabiles, H., Melkemi, M., Dornaika, F., Arganda-Carreras, I., Collard, D., & Scherpereel, A. (2021). Deep learning on chest x-ray images to detect and evaluate pneumonia cases at the era of covid-19. Journal of Medical Systems, 45(7), 1-10.
• Albawi, S., Mohammed, T. A., & Al-Zawi, S. (2017, August). Understanding of a convolutional neural network. In 2017 International Conference on Engineering and Technology (ICET) (pp. 1-6). Ieee.
• Özkan, İ. N. İ. K., & Ülker, E. (2017). Derin öğrenme ve görüntü analizinde kullanılan derin öğrenme modelleri. Gaziosmanpaşa Bilimsel Araştırma Dergisi, 6(3), 85-104.
• Guo, Y., Liu, Y., Oerlemans, A., Lao, S., Wu, S., & Lew, M. S. (2016). Deep learning for visual understanding: A review. Neurocomputing, 187, 27-48.
• Burkov, A. (2019). The hundred-page machine learning book (Vol. 1, pp. 3-5). Canada: Andriy Burkov.
• Hashmi, M. F., Katiyar, S., Keskar, A. G., Bokde, N. D., & Geem, Z. W. (2020). Efficient pneumonia detection in chest xray images using deep transfer learning. Diagnostics, 10(6), 417.