Abstract
Today, as in every field, the number of accessible medical devices for diagnosis and treatment is increasing with the rapid advancement of technology in the medical field. Especially the developments in medical images and its parallel development with artificial intelligence enable the emergence of decision support systems that help physicians. One of the most important reasons for this is that artificial intelligence minimizes human-induced errors in health services, as in most areas. Diagnosis by interpreting medical images by physicians is costly in terms of time. Diagnosing, classifying and automating medical images by using artificial intelligence techniques will ease the workload by providing decision support to physicians. In our study, a model was developed for the detection of covid-19 (2019-nCoV) by segmenting the lung tissue from CT Thorax (CT Chest) images, and the success of this procedure is discussed.
References
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Abstract
Günümüzde her alanda olduğu gibi medikal alanda da teknolojinin hızla ilerlemesiyle birlikte teşhis ve tedavi için ulaşılabilir medikal cihazların sayısı artmaktadır. Hastalar açısından, doğru zamanda, doğru medikal yaklaşımlarla alınan sağlık hizmeti oluşabilecek hayati riskleri önlemektedir. Özellikle tıbbi görüntülerdeki gelişmeler ve yapay zekâ ile paralel gelişimi hekimlere yardımcı, karar destek sağlayıcı sistemlerin ortaya çıkmasını sağlamaktadır. Bunun en önemli nedenlerinden biri yapay zekânın çoğu alanda olduğu gibi sağlık hizmetlerinde de insan kaynaklı hataları minimize etmesidir. Medikal görüntülerin hekimler tarafından yorumlanarak teşhis konulması zaman açısından maliyetli işlemlerdir. Medikal görüntülerin yapay zekâ tekniklerinden faydalanılarak teşhisin koyulması, sınıflandırılması ve otomatik hale getirilmesi hekimlere karar destek sağlayarak iş yükünü hafifletecektir. Çalışmamızda, Covid-19 (2019-nCoV) tespiti için BT Toraks (BT Göğüs) görüntülerinden akciğer dokusunun segmente edilerek bu işlemdeki başarısı ele alınan model geliştirilmiştir.
References
- https://covid19.who.int/, last accessed on 12 Sep 21.
- M. Toğaçar, B. Ergen, “Biyomedikal Görüntülerde derin Öğrenme ile Mevcut Yöntemlerin Kıyaslanması,” Fırat Üniversitesi Müh.Bilimleri Dergisi 2019; 31(1): 109-121.
- 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.
- CT scan images: URL: https://www.kaggle.com/plameneduardo/sarscov2-ctscan-dataset, last accessed on 15 Sep 2020.
- 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.
- L. Deng and D. Yu, “Deep Learning: Methods and Applications,” Found. Trends® Signal Process., vol. 7, no. 3–4, pp. 197–387, 2014.
- Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Chen, T., Recent advances in convolutional neural networks. Pattern Recognition, 2017.
- L. Deng and D. Yu, “Deep Learning: Methods and Applications,” Found. Trends® Signal Process., vol. 7, no. 3–4, pp. 197–387, 2014. FLEXChip Signal Processor (MC68175/D), Motorola, 1996.
- Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Chen, T., Recent advances in convolutional neural networks. Pattern Recognition, 2017.
- İnik,Ö.,Ülker E.,Derin Öğrenme ve Görüntü Analizinde Kullanılan Derin öğrenme Modelleri,Gaziosmanpaşa Bilimsel Araştırma Dergisi(GBAD),85-104,2017.
- 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.
- Computer Graphics Companion, Image Segmentation, 2003.
- Memon, N.A., Mirza, A.M. ve Gilani, S.A.M., Segmentation of Lungs from CT Scan Images for Early Diagnosis of Lung Cancer, Transactions on Engineering, Computing and Technology, 14, (2006) 228-233.
- J. Padhye, V. Firoiu, and D. Towsley, “A stochastic model of TCP Reno congestion avoidance and control,” Univ. of Massachusetts, Amherst, MA, CMPSCI Tech. Rep. 99-02, 1999.
Details
| Primary Language | Turkish |
|---|---|
| Subjects | Engineering |
| Journal Section | Articles |
| Authors | |
| Publication Date | November 30, 2021 |
| Published in Issue | Year 2021 Issue: 28 |
