Yeni Bir CT Görüntüleri Veri Kümesi ile Derin Öğrenme Tabanlı Covid-19 Tespiti: EFSCH-19

Year 2021, , 150 - 155, 01.12.2021

Oğuzhan Katar , Erkan Duman

https://doi.org/10.31590/ejosat.1021030

Cited By: 3

Abstract

COVID-19 pandemisi tüm dünyayı birçok yönden olumsuz etkiledi. Kurulduğu günden bu yana çeşitli yöntem ve yaklaşımlar geliştirilmiştir. Bu çözüm arayışlarının ortak özelliği, COVID-19 pandemisinin sosyal ve ekonomik zararlarını en aza indirmektir. Bu çalışmada, göğüs BT görüntülerinden COVID-19 hastalığının tespiti için derin öğrenme tabanlı modelimizi geliştirdik. Ancak literatürdeki çoğu çalışmada kullanılan halka açık veri setlerini kullanmadık. Çünkü halka açık veri setlerinde; düşük sayıda eleman, yanlış etiketlenmiş görüntüler ve dengesiz dağılım gibi sorunlar mevcut. Bu tür problemlerden dolayı modelimizin istenilen yüksek doğruluk değerlerine ulaşamayacağını düşündük. Elazığ Fethi Sekin Şehir Hastanesi'nden daha önce herhangi bir derin öğrenme çalışmasına dahil edilmemiş veri setimizi modelimizin eğitiminde ilk kez kullandık. Modelimiz 800 pozitif ve 800 normal göğüs BT görüntüsü ile eğitildi ve ardından rastgele seçilmiş 400 test görüntüsü ile test edildi. Bu testler sonucunda %97,5 doğruluk oranına ulaşılmıştır. Çalışmamızın sonuçları değerlendirildiğinde hekimlere COVID-19 hastalığının tespitinde yardımcı olabileceği düşünülmektedir.

Keywords

COVID-19, Derin öğrenme, Görüntü işleme, BT taramaları, ESA, Görüntü sınıflandırma, Teşhis.

Deep Learning Based Covid-19 Detection With A Novel CT Images Dataset: EFSCH-19

Abstract

COVID-19 pandemic has negatively affected the whole world in many ways. Since its inception, various methods and approaches have been developed. The common feature of these solution searches is minimizing the social and economic damages of the COVID-19 pandemic. In this article, we developed our deep learning-based model for the detection of COVID-19 disease from chest CT images. However, we did not use the publicly available datasets used in most studies in the literature. Because, in public datasets; there are problems such as low samples, incorrectly labeled images and unbalanced distribution. Due to such problems, we thought that our model would not reach the desired high accuracy values. We used our dataset, which has not been included in any deep learning study before, from Elazig Fethi Sekin City Hospital, for the first time in the training of our model. Our model was trained with 800 positive and 800 normal chest CT images and then tested with 400 randomly selected test images. As a result of these tests, accuracy rate of %97.5 was achieved. When the results of our study are evaluated, it is thought that it can help physicians in the detection of COVID-19 disease.

Keywords

COVID-19, Deep learning, Image processing, CT scans, CNN, Image Classification, Diagnosing

References

• Wang, D., Hu, B., Hu, C., Zhu, F., Liu, X., Zhang, J., ... & Peng, Z. (2020). Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus–infected pneumonia in Wuhan, China. Jama, 323(11), 1061-1069.
• Singhal, T. (2020). Uma revisão da doença de Coronavírus-2019 (COVID-19). Indian J Pediatr, 87, 281-286.
• Chan, J. F., Lau, S. K., To, K. K., Cheng, V. C., Woo, P. C., & Yuen, K. Y. (2015). Middle East respiratory syndrome coronavirus: another zoonotic betacoronavirus causing SARS-like disease. Clinical microbiology reviews, 28(2), 465-522.
• Wang, W., Xu, Y., Gao, R., Lu, R., Han, K., Wu, G., & Tan, W. (2020). Detection of SARS-CoV-2 in different types of clinical specimens. Jama, 323(18), 1843-1844.
• Li, L., Qin, L., Xu, Z., Yin, Y., Wang, X., Kong, B., ... & Xia, J. (2020). Artificial intelligence distinguishes COVID-19 from community acquired pneumonia on chest CT. Radiology.
• Singh, D., Kumar, V., & Kaur, M. (2020). Classification of COVID-19 patients from chest CT images using multi-objective differential evolution–based convolutional neural networks. European Journal of Clinical Microbiology & Infectious Diseases, 39(7), 1379-1389.
• Jashnani, K., Nargunde, R., Shah, Y., & Raul, N. (2021, June). COVID-19 Prediction from CT Scans using Deep-Learning. In 2021 International Conference on Communication information and Computing Technology (ICCICT) (pp. 1-6). IEEE.
• Carvalho, E. D., Carvalho, E. D., de Carvalho Filho, A. O., De Araújo, F. H. D., & Rabêlo, R. D. A. L. (2020, July). Diagnosis of COVID-19 in CT image using CNN and XGBoost. In 2020 IEEE Symposium on Computers and Communications (ISCC) (pp. 1-6). IEEE.
• Li, L., Qin, L., Xu, Z., Yin, Y., Wang, X., Kong, B., ... & Xia, J. (2020). Using artificial intelligence to detect COVID-19 and community-acquired pneumonia based on pulmonary CT: evaluation of the diagnostic accuracy. Radiology, 296(2), E65-E71.
• Song, Y., Zheng, S., Li, L., Zhang, X., Zhang, X., Huang, Z., ... & Yang, Y. (2021). Deep learning enables accurate diagnosis of novel coronavirus (COVID-19) with CT images. IEEE/ACM Transactions on Computational Biology and Bioinformatics.
• He, X., Wang, S., Chu, X., Shi, S., Tang, J., Liu, X., ... & Ding, G. (2021). Automated Model Design and Benchmarking of 3D Deep Learning Models for COVID-19 Detection with Chest CT Scans. arXiv preprint arXiv:2101.05442.
• Jin, S., Wang, B., Xu, H., Luo, C., Wei, L., Zhao, W., ... & Xu, W. (2020). AI-assisted CT imaging analysis for COVID-19 screening: Building and deploying a medical AI system in four weeks. MedRxiv.
• Chen, J., Wu, L., Zhang, J., Zhang, L., Gong, D., Zhao, Y., ... & Yu, H. (2020). Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography. Scientific reports, 10(1), 1-11.
• Karakanis, S., & Leontidis, G. (2021). Lightweight deep learning models for detecting COVID-19 from chest X-ray images. Computers in biology and medicine, 130, 104181.
• Prokop, M., Van Everdingen, W.,... & COVID-19 Standardized Reporting Working Group of the Dutch Radiological Society. (2020). CO-RADS: a categorical CT assessment scheme for patients suspected of having COVID-19 definition and evaluation. Radiology, 296(2), E97-E104.