Derin öğrenme mimarilerini kullanarak göğüs BT görüntülerinden otomatik Covid-19 tahmini

Yıl 2022, IOCENS’21 Konferansı Ek Sayısı, 76 - 88, 30.09.2022

Veysel Türk Hatice Çatal Reis Serhat Kaya

https://doi.org/10.17714/gumusfenbil.1002738

Cited By: 1

Öz

Makine öğrenmesi, son yıllarda hastalık tespiti ve segmentasyon araştırmalarında aktif olarak kullanılmaktadır. Son yıllarda insanlık, Koronavirus hastalığı 2019 (Covid-19) ile mücadele etmektedir. Göğüs-bilgisayarlı tomografi (BT) görüntüsü, olası Covid-19 hastalarını tespit etme de önemli bir araçtır. Bu çalışma, Derin Öğrenme (DÖ) algoritmaları kullanarak Covid-19 ve Covid-19 olmayan göğüs BT görüntülerini, sınıflandırmayı ve dört mimari kullanarak farklı parametrelerde başarılı sonuçlar elde edip edemeyeceğimizi araştırmayı amaçlamaktadır. Çalışma, kanıtlanmış pozitif Covid-19 CT görüntüleri üzerinde gerçekleştirildi ve görüntüler GitHub kamu platformundan elde edilmiştir. VGG16, VGG19, LeNet-5 ve MobileNet gibi dört farklı derin öğrenme mimarisi değerlendirildi. Performans değerlendirmelerinde ROC eğrisi, duyarlılık, doğruluk, F1-ölçütü, kesinlik ve RMSE kullanılmıştır. MobileNet modeli en iyi sonucu vermiştir sırasıyla; F1-ölçütü %95, doğruluk %95, kesinlik %100, duyarlılık %90, AUC %95 ve RMSE 0.23'tür. En düşük performansı ise; F1-ölçütü %90, doğruluk %89, kesinlik %90, duyarlılık%90, AUC %89 ve RMSE 0.32 ile VGG19 modeli vermiştir. Algoritmaların performansları karşılaştırıldığında en yüksek doğruluk sırasıyla MobileNet, LeNet-5, VGG16 ve VGG19'dan elde edilmiştir. Bu çalışma önerilen modeller çerçevesinde, Covid-19'u tespit etmek için derin öğrenme modellerinin kullanışlılığını göstermiştir. Bu nedenle araştırma, Covid-19 tespit çalışmalarında Tıp ve Mühendislik literatürüne katkı sağlayabilir.

Anahtar Kelimeler

Covid-19, Derin öğrenme, LeNet-5, MobileNet, VGG16, VGG19

Automatic prediction of covid-19 from chest- computed tomography (CT) images using deep learning architectures

Öz

Machine learning has been actively used in disease detection and segmentation in recent years. For the last few years, the world has been coping with the Coronavirus disease 2019 (COVID-19) pandemic. Chest-computerized tomography (CT) is often a meaningful way to detect and detect patients with possible COVID-19. This study aims to classify COVID-19 and non-COVID-19 chest-CT images using deep learning (DL) algorithms and investigate whether we can achieve successful results in different parameters using four architectures. The study was performed on proved positive COVID-19 CT images, and the datasets were obtained from the GitHub public platform. The study evaluated four different deep learning architectures of VGG16, VGG19, LeNet-5, and MobileNet. The performance evaluations were used with ROC curve, recall, accuracy, F1-score, precision, and Root Mean Square Error (RMSE). MobileNet model showed the best result; F1 score of 95%, the accuracy of 95%, the precision of 100%, recall of 90%, AUC of 95%, and RMSE of 0.23. On the other hand, VGG 19 model gave the lowest performance; F1 score of 90%, the accuracy of 89%, the precision of 90%, recall of 90%, AUC of 89%, and RMSE of 0.32. When the algorithms' performances were compared, the highest accuracy was obtained from MobileNet, LeNet-5, VGG16, and VGG19, respectively.
This study has proven the usefulness of deep learning models to detect COVID-19 in chest-CT images based on the proposed model framework. Therefore, it can contribute to the literature in Medical and Engineering in COVID-19 detection research.

Anahtar Kelimeler

COVID-19, Deep learning, LeNet-5, MobileNet, VGG16, VGG19

Kaynakça

• Acharya, U. R., Faust, O., Sree, S. V., Ghista, D. N., Dua, S., Joseph, P., Ahamed, V. I., Janarthanan, N., & Tamura, T. (2013). An integrated diabetic index using heart rate variability signal features for diagnosis of diabetes. Computer Methods in Biomechanics and Biomedical Engineering, 16(2), 222-234. https://doi.org/10.1080/10255842.2011.616945.
• Açıkgöz, Ö., & Günay, A. (2020). The early impact of the COVID-19 pandemic on the global and Turkish economy. Turkish Journal of Medical Sciences, 50(SI-1), 520-526. https://doi.org/10.3906/sag-2004-6.
• Akçay, M. Ş., Özlü, T., & Yılmaz, A. (2020). Radiological approaches to COVID-19 pneumonia. Turkish journal of medical sciences, 50(9), 604-610. https://doi.org/10.3906/sag-2004-160.
• 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.
• Bahdanau, D., Chorowski, J., Serdyuk, D., Brakel, P., & Bengio, Y. (2016). End-to-end attention-based large vocabulary speech recognition. In 2016 IEEE international conference on acoustics, speech and signal processing (ICASSP) (pp. 4945-4949). IEEE. https://doi.org/10.1109/ICASSP.2016.7472618.
• Bao, C., Liu, X., Zhang, H., Li, Y., & Liu, J. (2020). Coronavirus disease 2019 (COVID-19) CT findings: a systematic review and meta-analysis. Journal of the American college of radiology, 17(6), 701-709. https://doi.org/10.1016/j.jacr.2020.03.006.
• Caruso, D., Zerunian, M., Polici, M., Pucciarelli, F., Polidori, T., Rucci, C., Guido, G., Bracci, B., De Dominicis, C., & Laghi, A. (2020). Chest CT features of COVID-19 in Rome, Italy. Radiology, 296(2), E79-E85. https://doi.org/10.1148/radiol.2020201237.
• Çatal Reis, H. (2022). COVID-19 Diagnosis with Deep Learning. Ingeniería e Investigación, 42(1). https://doi.org/10.15446/ing.investig.v42n1.88825.
• Chung, M., Bernheim, A., Mei, X., Zhang, N., Huang, M., Zeng, X., Cui, J., Xu, W., Yang, Y., Fayad, Z. A., Jacobi, A., Li, K., Li, S., & Shan, H. (2020). CT imaging features of 2019 novel coronavirus (2019-nCoV). Radiology, 295(1), 202-207. https://doi.org/10.1148/radiol.2020200230.
• Deng, L. (2014). A tutorial survey of architectures, algorithms, and applications for deep learning. APSIPA transactions on Signal and Information Processing, 3. https://doi.org/10.1017/atsip.2013.9.
• Docevski, M., Zdravevski, E., Lameski, P., & Kulakov, A. (2018). Towards music generation with deep learning algorithms. in CiiT 2018 - 15th International Conference on Informatics and Information Technologies. Mavrovo, Macedonia.
• Doğan, F., & Türkoğlu, İ. (2019). A compilation of deep learning models and application areas. Dicle University Journal of Engineering, 10(2), 409-445. https://doi.org/10.24012/dumf.411130.
• El Asnaoui, K., Chawki, Y., & Idri, A. (2021). Automated methods for detection and classification pneumonia based on x-ray images using deep learning. In Artificial intelligence and blockchain for future cybersecurity applications (pp. 257-284). Springer, Cham. https://doi.org/10.1007/978-3-030-74575-2_14.
• Güner, H. R., Hasanoğlu, İ., & Aktaş, F. (2020). COVID-19: Prevention and control measures in community. Turkish Journal of medical sciences, 50(9), 571-577. https://doi.org/10.3906/sag-2004-146.
• Hajian-Tilaki, K. (2013). Receiver operating characteristic (ROC) curve analysis for medical diagnostic test evaluation. Caspian journal of internal medicine, 4(2), 627-635.
• Hemdan, E. E. D., Shouman, M. A., & Karar, M. E. (2020). Covidx-net: A framework of deep learning classifiers to diagnose covid-19 in x-ray images. arXiv preprint arXiv:2003.11055.
• Huang, C., Wang, Y., Li, X., Ren, L., Zhao, J., Hu, Y., Zhang, L., Fan, G., Xu, J., Gu, X., Cheng, Z., Yu, T., Xia, J., Wei, Y., Wu, W., Xie, X., Yin, W., Li, H., Liu, M., Xiao, Y., Gao, H., Guo, L., Xie, J., Wang, G., Jiang, R., Gao, Z., Jin, Q., Wang, J., & Cao, B. (2020). Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. The Lancet, 395(10223), 497-506. https://doi.org/10.1016/S0140-6736(20)30183-5.
• Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., & Adam, H. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861.
• İnik, Ö., & Ülker, E. (2017). Deep learning and deep learning models used in image analysis. Gaziosmanpasa Journal of Scientific Research, 6(3), 85-104. https://dergipark.org.tr/tr/pub/gbad/issue/31228/330663.
• Khan, E., Rehman, M. Z. U., Ahmed, F., Alfouzan, F. A., Alzahrani, N. M., & Ahmad, J. (2022). Chest X-ray classification for the detection of COVID-19 using deep learning techniques. Sensors, 22(3), 1211. https://doi.org/10.3390/s22031211.
• Kim, M., Yun, J., Cho, Y., Shin, K., Jang, R., Bae, H. J., & Kim, N. (2019). Deep learning in medical imaging. Neurospine, 16(4), 657-668. https://doi.org/10.14245/ns.1938396.198.
• Kogilavani, S. V., Prabhu, J., Sandhiya, R., Kumar, M. S., Subramaniam, U., Karthick, A., Muhibbullah, M., & Imam, S. B. (2022). COVID-19 detection based on lung CT scan using deep learning techniques. Computational and Mathematical Methods in Medicine, 2022, 1-13. https://doi.org/10.1155/2022/7672196.
• LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444. https://doi.org/10.1038/nature14539.
• LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324. https://doi.org/10.1109/5.726791.
• Lei, J., Li, J., Li, X., & Qi, X. (2020). CT imaging of the 2019 novel coronavirus (2019-nCoV) pneumonia. Radiology, 295(1), 18-18. https://doi.org/10.1148/radiol.2020200236.
• Luz, E., Silva, P., Silva, R., Silva, L., Guimarães, J., Miozzo, G., Moreira, G., & Menotti, D. (2022). Towards an effective and efficient deep learning model for COVID-19 patterns detection in X-ray images. Research on Biomedical Engineering, 38(1), 149-162. https://doi.org/10.1007/s42600-021-00151-6.
• Murugan, R., Goel, T., Mirjalili, S., & Chakrabartty, D. K. (2021). WOANet: Whale optimized deep neural network for the classification of COVID-19 from radiography images. Biocybernetics and Biomedical Engineering, 41(4), 1702-1718. https://doi.org/10.1016/j.bbe.2021.10.004.
• Nasir, M. U., Roberts, J., Muller, N. L., Macri, F., Mohammed, M. F., Akhlaghpoor, S., Parker, W., Eftekhari, A., Rezaei, S., Mayo, J., & Nicolaou, S. (2020). The role of emergency radiology in COVID-19: From preparedness to diagnosis. Canadian Association of Radiologists Journal, 71(3), 293-300. https://doi.org/10.1177/0846537120916419.
• Özyurt, F. (2020). Efficient deep feature selection for remote sensing image recognition with fused deep learning architectures. The Journal of Supercomputing, 76(11), 8413-8431. https://doi.org/10.1007/s11227-019-03106-y.
• Patnaik, S. K., Sidhu, M. S., Gehlot, Y., Sharma, B., & Muthu, P. (2018). Automated skin disease identification using deep learning algorithm. Biomedical & Pharmacology Journal, 11(3), 1429-1436. http://dx.doi.org/10.13005/bpj/1507.
• Sae-Lim, W., Wettayaprasit, W., & Aiyarak, P. (2019). Convolutional neural networks using MobileNet for skin lesion classification. In 2019 16th international joint conference on computer science and software engineering (JCSSE) (pp. 242-247). IEEE. https://doi.org/10.1109/JCSSE.2019.8864155.
• Saeedi, A., Saeedi, M., & Maghsoudi, A. (2020). A novel and reliable deep learning web-based tool to detect covid-19 infection from chest ct-scan. arXiv preprint arXiv:2006.14419.
• Sarraf, S., & Tofighi, G. (2016). Classification of alzheimer's disease using fmri data and deep learning convolutional neural networks. arXiv preprint arXiv:1603.08631.
• Sejnowski, T. J., & Tesauro, G. (1989). The Hebb rule for synaptic plasticity: algorithms and implementations. In Neural models of plasticity (pp. 94-103). Academic Press. https://doi.org/10.1016/B978-0-12-148955-7.50010-2.
• Shi, H., Han, X., Jiang, N., Cao, Y., Alwalid, O., Gu, J., Fan, Y., & Zheng, C. (2020). Radiological findings from 81 patients with COVID-19 pneumonia in Wuhan, China: a descriptive study. The Lancet Infectious Diseases, 20(4), 425–434. https://doi.org/10.1016/S1473-3099(20)30086-4.
• Shin, H. C., Roth, H. R., Gao, M., Lu, L., Xu, Z., Nogues, I., Yao, J., Mollura, D., & Summers, R. M. (2016). Deep Convolutional Neural Networks for Computer-Aided Detection: CNN architectures, dataset characteristics and transfer learning. IEEE Transactions on Medical Imaging, 35(5), 1285–1298. https://doi.org/10.1109/TMI.2016.2528162.
• Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
• Song, Y., Zheng, S., Li, L., Zhang, X., Zhang, X., Huang, Z., Chen, J., Wang, R., Zhao, H., Chong, Y., Shen, J., Zha, Y., & Yang, Y. (2021). Deep learning enables accurate diagnosis of novel coronavirus (Covid-19) with ct ımages. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 18(6), 2775–2780. https://doi.org/10.1109/tcbb.2021.3065361.
• Swapna, G., Vinayakumar, R., & Soman, K. P. (2018). Diabetes detection using deep learning algorithms. ICT express, 4(4), 243-246. https://doi.org/10.1016/j.icte.2018.10.005.
• Wang, G., Li, W., Zuluaga, M. A., Pratt, R., Patel, P. A., Aertsen, M., Doel, T., David, A. L., Deprest, J., Ourselin, S., & Vercauteren, T. (2018). Interactive Medical ıiage segmentation using deep learning with ımage-specific fine tuning. IEEE Transactions on Medical Imaging, 37(7), 1562–1573. https://doi.org/10.1109/tmi.2018.2791721.
• WHO-World Health Organization. (2021, 29 September) https://www.who.int/emergencies/diseases/novel-coronavirus-2019.
• Xu, J. X., Lin, T. C., Yu, T. C., Tai, T. C., & Chang, P. C. (2018). Acoustic scene classification using reduced MobileNet architecture. In 2018 IEEE International Symposium on Multimedia (ISM) (pp. 267-270). IEEE. https://doi.org/10.1109/ISM.2018.00038.
• Yan, Y., Chen, M., Sadiq, S., & Shyu, M. L. (2017). Efficient imbalanced multimedia concept retrieval by deep learning on spark clusters. International Journal of Multimedia Data Engineering and Management (IJMDEM), 8(1), 1-20. https://doi.org/10.4018/IJMDEM.2017010101.
• Yang, X., He, X., Zhao, J., Zhang, Y., Zhang, S., & Xie, P. (2020). COVID-CT-dataset: a CT scan dataset about COVID-19. arXiv preprint arXiv:2003.13865.
• Zheng, C., Deng, X., Fu, Q., Zhou, Q., Feng, J., Ma, H., Liu, W., & Wang, X. (2020). Deep learning-based detection for COVID-19 from chest CT using weak label. MedRxiv, doi.org/10.1101/2020.03.12.20027185.