Öz
COVID-19 outbreak first emerged on December 31, 2019 in Wuhan, China. The Novel Coronavirus Disease is caused by the SAR-CoV-2 virus, which causes respiratory symptoms such as fever, cough, and shortness of breath. While scientists continue their fight against SARS-CoV-2 (2019-nCoV), one of the deadliest viruses in the last century, with tests to help diagnosis and prognosis, drug and vaccine discovery, Information Technologies mostly continues to work on early diagnosis, prognosis and prediction. The aim is to reveal systems with low margin of error that will alleviate the workload of healthcare professionals, as well as early diagnosis and initiation of treatment.Deep Learning and Computer vision is the most commonly used. Two class (covid, non-covid) classification solution, using the Artificial Intelligence Techniques, have been examined in this paper. CNN architecture, has been created to develop an model to disease detection process of COVID-19(2019-nCoV) virus infected patients from CT images consisting of NON-COVID and COVID classes. We have proposed the classifying of CT images using the 2 Convolutions and pool layers with the model which shortening the time for classification and achieved an accuracy of nearly 95.77%. Results show that the used model attains provide highly satisfying results and can be used for any image classification.
Anahtar Kelimeler
Deep Learning CNN 2019-nCoV SARS-CoV-2 COVID Classification Chest CT images
Kaynakça
- [1] https://covid19.who.int/, last accessed on 9 Jun 21.
- [2] Rajkumar RP. COVID-19 and mental health: a review of the existing literature. Asian J Psychiatry 2020;52 . https://doi.org/10.1016/j.ajp.2020.102066 102066.
- [3] Luo M, Guo L, Yu M, Jiang W, Wang H. The psychological and mental impact of coronavirus disease 2019 (COVID-19) on medical staff and general public – a systematic review and meta-analysis. Psychiatry Res 2020;291 . https://doi.org/10.1016/j.psychres.2020.113190 113190.
- [4] URL:https://radiologyassistant.nl/chest/covid-19/covid19-imaging-findings, last accessed on 19 Oct 20.
- [5] Fraiwan L, Hassanin O, Fraiwan M, Khassawneh B, Ibnian AM, Alkhodari M. Automatic identification of respiratory diseases from stethoscopic lung sound signals using ensemble classifiers. Biocybern Biomed Eng 2021;41(1):1–14. https://doi.org/10.1016/j.bbe.2020.11.003.
- [6] Chen, X., Xiang, S., Liu, C. L., & Pan, C. H., Vehicle detection in satellite images by hybrid deep convolutional neural networks, IEEE Geoscience and remote sensing letters, 11 (10), 1797-1801, 2014.
- [7] Özkan, İ. & Ülker, E., “Derin Öğrenme ve Görüntü Analizinde Kullanılan Derin Öğrenme Modelleri”, Gaziosmanpaşa Bilimsel Araştırma Dergisi, 6(3), 85-104, 2017.
- [8] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu Perez, J.,Lo,B., & Yang, G.Z., “Deep learning for health informatics”. IEEE Journal of Biomedical and health informatics, 21(1), 4-21, 2016.
- [9] L. Deng and D. Yu, “Deep Learning: Methods and Applications,” Found. Trends® Signal Process., vol. 7, no. 3–4, pp. 197–387, 2014.
- [10] Nurettin Öner, İsmail Ağırbaş, Bilgisayar Tomografisi ve Manyetik Rezonans Görüntülemenin Sağlıkta Teknoloji Değerlendirme ve Maliyet-Fayda Analizi ile Değerlendirilmesi, Sağ. Perf. Kal. Derg (8), 147-163, 2014
- [11] Zhou, S. K., Greenspan, H., and Shen, D., "Deep Learning for Medical Image Analysis", 1. Ed., Academic Press, London; San Diego, 458 (2017).
- [12] Shen, D., Wu, G., and Suk, H.-I., "Deep learning in medical image analysis", Annual Review Of Biomedical Engineering, 19 (1): 221–248 (2017).
- [13] Internet: Deep Learning, "What is the effect of adjusting BatchSize on the training effect?" https://www.programmersought.com/article/2840518935/
- [14] Frid-Adar,M.,Diamant, I.,Klang E.,Amitai,M.,Goldberger J.,Greenspan H.,GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification.Neurocomputing. 321-331,2018.
- [15] Internet: ujjwalkarn, "An Intuitive Explanation of Convolutional Neural Networks”, https://ujjwalkarn.me/2016/08/11/intuitive-explanation-convnets/ (2018).
- [16] Sabour, S., Frosst, N., and Hinton, G. E., "Dynamic routing between capsules", ArXiv:1710.09829 [Cs], (2017).
Öz
COVID-19 salgını ilk olarak 31 Aralık 2019'da Çin'in Wuhan kentinde ortaya çıktı. Yeni Koronavirüs Hastalığına ateş, öksürük ve nefes darlığı gibi solunum semptomlarına neden olan SAR-CoV-2 virüsü neden olur. Bilim insanları, son yüzyılın en ölümcül virüslerinden biri olan SARS-CoV-2 (2019-nCoV) ile tanı ve prognoza yardımcı olacak testler, ilaç ve aşı keşfi ile mücadelelerini sürdürürken, Bilgi Teknolojileri de çoğunlukla erken teşhis konusunda çalışmalarını sürdürüyor. Amaç, sağlık çalışanlarının iş yükünü hafifletecek, erken teşhis ve tedaviye başlamayı sağlayacak, hata payı düşük sistemleri ortaya çıkarmaktır. Bu makalede, Yapay Zeka Teknikleri kullanılarak iki sınıflı (covid, covid olmayan) sınıflandırma çözümü incelenmiştir. CNN mimarisi, COVID-19(2019-nCoV) virüsü ile enfekte hastaların, KOVİD OLMAYAN ve COVID sınıflarından oluşan BT görüntülerinden hastalık tespit sürecine bir model geliştirmek için oluşturulmuştur. Sınıflandırma süresini kısaltan ve yaklaşık %95.77 doğruluk sağlayan model ile BT görüntülerinin 2 Konvolüsyon ve havuz katmanları kullanılarak sınıflandırılmasını önerdik. Sonuçlar, kullanılan modelin oldukça tatmin edici sonuçlar verdiğini ve herhangi bir görüntü sınıflandırması için kullanılabileceğini göstermektedir.
Anahtar Kelimeler
CNN 2019-nCoV SARS-CoV-2 COVID Derin öğrenme Sınıflandırma CT Görüntüleri
Kaynakça
- [1] https://covid19.who.int/, last accessed on 9 Jun 21.
- [2] Rajkumar RP. COVID-19 and mental health: a review of the existing literature. Asian J Psychiatry 2020;52 . https://doi.org/10.1016/j.ajp.2020.102066 102066.
- [3] Luo M, Guo L, Yu M, Jiang W, Wang H. The psychological and mental impact of coronavirus disease 2019 (COVID-19) on medical staff and general public – a systematic review and meta-analysis. Psychiatry Res 2020;291 . https://doi.org/10.1016/j.psychres.2020.113190 113190.
- [4] URL:https://radiologyassistant.nl/chest/covid-19/covid19-imaging-findings, last accessed on 19 Oct 20.
- [5] Fraiwan L, Hassanin O, Fraiwan M, Khassawneh B, Ibnian AM, Alkhodari M. Automatic identification of respiratory diseases from stethoscopic lung sound signals using ensemble classifiers. Biocybern Biomed Eng 2021;41(1):1–14. https://doi.org/10.1016/j.bbe.2020.11.003.
- [6] Chen, X., Xiang, S., Liu, C. L., & Pan, C. H., Vehicle detection in satellite images by hybrid deep convolutional neural networks, IEEE Geoscience and remote sensing letters, 11 (10), 1797-1801, 2014.
- [7] Özkan, İ. & Ülker, E., “Derin Öğrenme ve Görüntü Analizinde Kullanılan Derin Öğrenme Modelleri”, Gaziosmanpaşa Bilimsel Araştırma Dergisi, 6(3), 85-104, 2017.
- [8] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu Perez, J.,Lo,B., & Yang, G.Z., “Deep learning for health informatics”. IEEE Journal of Biomedical and health informatics, 21(1), 4-21, 2016.
- [9] L. Deng and D. Yu, “Deep Learning: Methods and Applications,” Found. Trends® Signal Process., vol. 7, no. 3–4, pp. 197–387, 2014.
- [10] Nurettin Öner, İsmail Ağırbaş, Bilgisayar Tomografisi ve Manyetik Rezonans Görüntülemenin Sağlıkta Teknoloji Değerlendirme ve Maliyet-Fayda Analizi ile Değerlendirilmesi, Sağ. Perf. Kal. Derg (8), 147-163, 2014
- [11] Zhou, S. K., Greenspan, H., and Shen, D., "Deep Learning for Medical Image Analysis", 1. Ed., Academic Press, London; San Diego, 458 (2017).
- [12] Shen, D., Wu, G., and Suk, H.-I., "Deep learning in medical image analysis", Annual Review Of Biomedical Engineering, 19 (1): 221–248 (2017).
- [13] Internet: Deep Learning, "What is the effect of adjusting BatchSize on the training effect?" https://www.programmersought.com/article/2840518935/
- [14] Frid-Adar,M.,Diamant, I.,Klang E.,Amitai,M.,Goldberger J.,Greenspan H.,GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification.Neurocomputing. 321-331,2018.
- [15] Internet: ujjwalkarn, "An Intuitive Explanation of Convolutional Neural Networks”, https://ujjwalkarn.me/2016/08/11/intuitive-explanation-convnets/ (2018).
- [16] Sabour, S., Frosst, N., and Hinton, G. E., "Dynamic routing between capsules", ArXiv:1710.09829 [Cs], (2017).
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Konular | Yapay Zeka |
| Bölüm | PAPERS |
| Yazarlar | |
| Yayımlanma Tarihi | 20 Ekim 2021 |
| Gönderilme Tarihi | 3 Eylül 2021 |
| Kabul Tarihi | 20 Eylül 2021 |
| Yayımlandığı Sayı | Yıl 2021 Cilt: IDAP-2021 : 5th International Artificial Intelligence and Data Processing symposium Sayı: Special |
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