ÇEKİŞMELİ ÜRETİCİ AĞLAR VE TRANSFER ÖĞRENİMİ KULLANILARAK GÖĞÜS X-RAY GÖRÜNTÜLERİNDEN COVID-19 TESPİTİ ÜZERİNE BİR DERLEME

Yıl 2022, , 328 - 340, 23.03.2022

Meltem Kurt Pehlivanoğlu , Uğur Kadir Arabacı

https://doi.org/10.21923/jesd.955916

Öz

COVID-19 pandemisi ölümcül salgınlardan biridir. Hastalığın daha fazla yayılmasını azaltmak için yapay zekâya dayalı alternatif test yöntemleri değerlendirilmiştir. Viral bakteriyel zatürre (pnömoni) ile göğüs X-Ray görüntüleri COVID-19 hakkında önemli bilgiler sağlar. Bir yapay zekâ sistemi, radyologların bu göğüs röntgeni görüntülerinden COVID-19'u tespit etmesine yardımcı olabilir. Çekişmeli Üretici Ağlar (Generative Adversarial Networks-GANs) görüntü veri kümesinin genişletilmesi, yüksek çözünürlüklü görüntü elde etme, bir görüntüdeki desenin başka bir görüntüye transfer edilmesi gibi alanlarda kullanılır. Bu çalışmada, literatürde verilen göğüs X-Ray görüntüleri üzerinden COVID-19 tespiti yapan güncel çalışmalar kapsamlı olarak tartışılmıştır. Ayrıca bu çalışmalarda kullanılan veri kümelerinin özellikleri, GAN ile sentetik görüntülerin üretimi ve transfer öğrenme mimarileri üzerinde durulmuştur. Çalışma, göğüs X-Ray görüntüleri üzerinde COVID-19 tespiti yapan diğer çalışmalar için karşılaştırmalı bir rapor sağlamayı amaçlamaktadır.

Anahtar Kelimeler

COVID-19 Tespiti, Göğüs X-Ray Görüntüleri, Makine Öğrenmesi, Derin Öğrenme, Transfer Öğrenimi

Destekleyen Kurum

TÜBİTAK 2209-A Üniversite Öğrencileri Araştırma Projeleri Destekleme Programı

Proje Numarası

1919B012005809 Başvuru Numaralı Proje

A SURVEY ON COVID-19 DETECTION FROM CHEST X-RAY IMAGES USING GENERATIVE ADVERSIAL NETWORKS AND TRANSFER LEARNING

Öz

The pandemic related to the COVID-19 is one of the deadly epidemics. To reduce the further spread of the disease the alternative testing methods based on artificial intelligence have been evaluated. The chest X-Ray images with viral bacterial pneumonia provide remarkable information about COVID-19. An artificial intelligence system can help radiologists to detect COVID-19 from these chest X-Ray images. Generative Adversarial Networks (GANs) are used in areas such as expanding the image dataset (image augmentation), obtaining high-resolution images, transferring a pattern from one image to another. In this paper, the current studies which detect COVID-19 from the chest X-Ray images have been comprehensively discussed. Moreover, the properties of the datasets used in these studies, generating synthetic images with GAN and transfer learning approaches have been emphasized. This paper aims to provide a comprehensive report for other studies which detect COVID-19 from the chest X-Ray images.

Anahtar Kelimeler

COVID-19 Detection, Chest X-Ray Images, Machine Learning, Deep Learning, Transfer Learning

Proje Numarası

1919B012005809 Başvuru Numaralı Proje

Kaynakça

• Actualmed COVID-19 Chest X-ray Dataset Initiative, https://github.com/agchung/Actualmed-COVID-chestxray-dataset, 2020.
• Ahmed A., et al. Pneumonia Sample X-Rays, GitHub, 2019. https://www.kaggle.com /ahmedali2019/pneumonia-sample-xrays.
• Ahmed S., Yap M.H., et al. 2020. medRxiv.07.11.20149112; doi: https://doi.org/10.1101/2020.07.11.20149112
• Apostolopoulos I. D., Mpesiana T. A., 2020. Covid-19: automatic detection from x-ray images utilizing transfer learning with convolutional neural 15 networks, Physical and Engineering Sciences in Medicine, vol. 43, pp.635–640.
• Bozkurt, F. 2021. Derin Öğrenme Tekniklerini Kullanarak Akciğer X-Ray Görüntülerinden COVID-19 Tespiti. Avrupa Bilim ve Teknoloji Dergisi, vol. 24, pp. 149-156.
• Bozkurt, F. and Bayram, E. 2021. Local Binary Pattern Based COVID-19 Detection Method Using Chest X-Ray Images. 29th Signal Processing and Communications Applications Conference (SIU), pp. 1-4, doi: 10.1109/SIU53274.2021.9477796.
• Bustos A., Pertusa A. 2020. Padchest: A large chest x-ray image dataset with multi-label annotated reports. Medical Image Analysis, page 101797.
• Chest Imaging, https://threadreaderapp.com/thread/1243928581983670272.html, 2021.
• Chest Imaging, https://twitter.com/ChestImaging/status/1243928581983670272, 2021.
• Chouhan, V., Singh, S.K., et al. 2020 A novel transfer learning based approach for pneumonia detection in chest X-ray images. Appl. Sci. 10, 559.
• Chowdhury, M.E.H., et al. ., 2020. Can AI Help in Screening Viral and COVID-19 Pneumonia?. IEEE Access, vol. 8, pp. 132665-132676.arXiv preprint arXiv:2003.13145.
• Cohen J. P., Morrison P., et al. 2020. Covid-19 image data collection: Prospective predictions are the future. arXiv preprint arXiv:2006.11988, 2020.
• Covid Data Save Lives, https://www.hmhospitales.com/coronavirus/covid-data-save-lives/english-version, 2020.
• COVID-19 CT scans, https://www.kaggle.com/andrewmvd/covid19-ct-scans, 2020.
• COVID-19 Image Repository, https://github.com/ml-workgroup/covid-19-image-repository, 2020.
• COVID-19 X rays, https://www.kaggle.com/andrewmvd/convid19-x-rays, 2020.
• COVID-19-AR, https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=70226443, 2020.
• COVID-chestxray-dataset, https://github.com/agchung/Figure1-COVID-chestxray-dataset, 2020.
• Das, A.K., Ghosh, S., Thunder, S. et al. 2021. Automatic COVID-19 detection from X-ray images using ensemble learning with convolutional neural network. Pattern Anal Applic (2021). https://doi.org/10.1007/s10044-021-00970-4
• Dilbag S, Kumar V, Vaishali, Kaur M. 2020. “Classification of covid-19 patients from chest CT images using multi-objective differential evolution–based convolutional neural networks. Eur J Clin Microbiol Infect Dis” 1–15. https://doi.org/10.1007/s10096-020-03901-z.
• Farooq M., Hafeez A., 2020. Covid-resnet: A deep learning framework for screening of covid19 from radiographs. arXiv preprint arXiv:2003.14395.
• Goodfellow, I.J., Pouget-Abadie J. et al.. Generative adversarial nets. In NIPS, 2014.
• Hammoudi K., et al. 2020. Deep learning on chest x-ray images to detect and evaluate pneumonia cases at the era of covid-19. arXiv preprint arXiv:2004.03399.
• He K., Zhang X., et al. 2016. Deep Residual Learning for Image Recognition,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778
• Hemdan et al. 2020. “COVIDX-Net: A Framework of Deep Learning Classifiers to Diagnose COVID-19 in X-Ray Images”, arXiv 2003.11055.
• Hosseiny M., et al. 2020. Radiology perspective of coronavirus disease 2019 (COVID-19): lessons from severe acute respiratory syndrome and Middle East respiratory syndrome, Am. J. Roentgenol. (2020) 1–5, https://doi.org/10.2214/AJR.20.22969.
• Huang G., Liu Z., et al. 2017. Densely Connected Convolutional Networks. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 2261–2269.
• Irvin J. Et al. 2019. Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 33, pages 590–597.
• Ismael, A. M., & Şengür, A. 2021. Deep learning approaches for COVID-19 detection based on chest X-ray images. Expert Systems with Applications, 164, 114054.
• Jaeger S., Candemir S., et al. 2014. Two public chest x-ray datasets for computer-aided screening of pulmonary diseases. Quantitative imaging in medicine and surgery, 4(6):475.
• Jain, R., Gupta, M., Taneja, S., & Hemanth, D. J. 2021. Deep learning based detection and analysis of COVID-19 on chest X-ray images. Applied Intelligence, vol. 51(3), pp. 1690-1700.
• Kermany D., Zhang K., et al. 2018. Labeled optical coherence tomography (oct) and chest x-ray images for classification. Mendeley data, 2.
• Khalifa N. E. M., Taha M. H. N., et al. 2020. Detection of Coronavirus (COVID-19) Associated Pneumonia based on Generative Adversarial Networks and a Fine-Tuned Deep Transfer Learning Model using Chest X-ray Dataset, arXiv: 2004.01184
• Khan AI, Shah JL, Bhat MM. 2020. CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images. Comput Methods Programs Biomed.
• Krizhevsky A., Sutskever I., et al. 2012. ImageNet classification with deep convolutional neural networks, in Proceedings of the 25th International Conference on Neural Information Processing Systems, 2012, pp. 1097–1105.
• Loey M. et al. 2020. “Within the lack of chest covid-19 x-ray dataset: A novel detection model based on gan and deep transfer learning,” Symmetry, vol. 12, no. 4, p. 651.
• Mooney, P., Chest X-Ray Images (Pneumonia). https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia, 2018.
• Motamed S., Rogalla P., Khalvati F. 2021. Data Augmentation Using Generative Adversarial Networks (GANs) For GAN-Based Detection Of Pneumonia And COVID-19 In Chest X-Ray Images. doi:10.21203/rs.3.rs-146161/v1
• NIH, https://nihcc.app.box.com/v/ChestXray-NIHCC, 2021.
• Öksüz C., Urhan O. ve Güllü M. K. 2020. Ensemble-CVDNet: A Deep Learning based End-to-End Classification Framework for COVID-19 Detection using Ensembles of Networks. arXiv:2012.09132
• Openi, https://openi.nlm.nih.gov/, 2020.
• Öztürk T., Talo M., et al. 2020. Automated detection of covid-19 cases using deep neural networks with x-ray images, Computers in Biology and Medicine, p. 103792.
• Patel, P. Chest X-ray (Covid-19 & Pneumonia), Accessed at: https://www.kaggle.com/prashant268/chest-xray-covid19-pneumonia, 2021.
• Pathak Y., Shuklab P.K., et al. 2020. Deep Transfer Learning Based Classification Model for COVID-19 Disease, https://doi.org/10.1016/j.irbm.2020.05.003
• Polat Ç, Karaman O, Karaman C, Korkmaz G, Balcı MC, Kelek SE. 2021. COVID-19 diagnosis from chest X-ray images using transfer learning: Enhanced performance by debiasing dataloader. J Xray Sci Technol. ;29(1):19-36. doi: 10.3233/XST-200757. PMID: 33459685; PMCID: PMC7990426.
• Rasheed, J., Hameed, A.A., Djeddi, C. et al. 2021. A machine learning-based framework for diagnosis of COVID-19 from chest X-ray images. Interdiscip Sci Comput Life Sci 13, 103–117
• Redmon J., Farhadi A. 2017. Yolo9000: better, faster, stronger. arXiv preprint, 2017.
• Sahlol, A.T., Yousri, D., Ewees, A.A. et al. 2020. COVID-19 image classification using deep features and fractional-order marine predators algorithm. Sci Rep 10, 15364 (2020). https://doi.org/10.1038/s41598-020-71294-2
• Sethy, P. K., & Behera, S. K. 2020. Detection of coronavirus Disease (COVID-19) based on Deep Features.
• Shell, J., Coupland S. 2012. Towards fuzzy transfer learning for intelligent environments, in Ambient Intelligence, 2012.7683: p. 145-160
• Shih, G., Wu, C.C., et al. 2019. Augmenting the national institutes of health chest radiograph dataset with expert annotations of possible pneumonia. Radiology: Artificial Intelligence, 1(1):e180041.
• Signoroni A., Savardi M. et al. 2020 End-to-end learning for semiquantitative rating of covid-19 severity on chest x-rays. arXiv 2006.04603
• Simonyan K., Zisserman A., 2014. Very Deep Convolutional Networks for Large-Scale Image Recognition. pp. 1–14.
• Singh, A.K., Kumar, A., Mahmud, M. et al. 2021 COVID-19 Infection Detection from Chest X-Ray Images Using Hybrid Social Group Optimization and Support Vector Classifier. https://doi.org/10.1007/s12559-021-09848-3
• SIRM. https://www.sirm.org/category/senza-categoria/covid-19/,2021.
• Szegedy C., Vincent V.V. et al. 2016. Rethinking the inception architecture for computer vision, in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2818–2826.
• Tabik, S., Gomez-Rios, A., et al., 2020. Covidgr dataset and covid-sdnet methodology for predicting covid-19 based on chest x-ray images. arXiv preprint arXiv:2006.01409.
• Tan M., Le Q. V. 2019. Efficientnet: Rethinking model scaling for convolutional neural networks. arXiv preprint arXiv:1905.11946.
• Toğaçar M., Ergen B., Cömert ., 2020. Covid-19 detection using deep ¨ learning models to exploit social mimic optimization and structured chest x-ray images using fuzzy color and stacking approaches. Computers in Biology and Medicine, p. 103805.
• Vayá I. et al. 2020. Bimcv covid-19+: a large annotated dataset of rx and ct images from covid-19 patients. arXiv preprint arXiv:2006.01174,
• VGG-19, https://keras.io/api/applications/vgg/, 2021.
• Waheed A., et al. 2020. Covidgan: Data augmentation using auxiliary classifier gan for improved covid-19 detection, IEEE Access, vol. 8, pp. 91 916– 91 923, 2020.
• Wang X., Peng Y.,et al. 2017. Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases, in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2097-2106.
• Wang, Z.Q.L.L. and Wong. A. 2020. Covid-Net: A tailored deep convolutional neural network design for detection of covid-19 cases from chest radiography images.
• Ying S., Zhen S., et al. 2020. Deep learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) with CT images. https://doi.org/10.1101/2020.02.23.20026930
• Zebin, T., Rezvy, S., 2021. COVID-19 detection and disease progression visualization: Deep learning on chest X-rays for classification and coarse localization. Appl Intell 51, 1010–1021.
• Zhang X., Zhou X., M. Lin, et al. 2017. Shufflenet: An extremely efficient convolutional neural network for mobile devices. arXiv:1707.01083.
• Zunair et al. 2021. Synthesis of COVID-19 chest X-rays using unpaired image-to-image translation. Social Network Analysis and Mining 11, 23. doi:10.1007/s13278-021-00731-5.