Yapay Zekâ ve Tıbbi Görüntüleme Teknolojilerine Genel Bakış

Furkan Atlan; İhsan Pençe

Review

Yapay Zekâ ve Tıbbi Görüntüleme Teknolojilerine Genel Bakış

Year 2021, Volume: 5 Issue: 1, 207 - 230, 29.06.2021

Furkan Atlan İhsan Pençe

Abstract

Günümüzde yapay zekânın kullanıldığı alanlar her geçen gün artmakta olup, bu alanlardan biri de sağlık sektörüdür. Özellikle görüntü işlemede oldukça başarılı sonuçlar vermesi sebebi ile yapay zekânın bir alt dalı olan derin öğrenme, tıbbi görüntülerin işlenmesinde ve yorumlanmasında sıkça tercih edilmektedir. Her ne kadar tıbbi görüntüleme teknolojilerinin gelişmesi ile hastalık tanısı ve teşhisi gibi işlemlerdeki doğruluk oranı artsa da bu görüntülerin uzmanlar tarafından doğru bir şekilde yorumlanması zaman açısından maliyetli ve tedavi süreci açısından da olumsuz bir durum sergilemektedir. Bu sebeple, yapay zekâ kullanılarak otomatik tanı sistemleri oluşturulmakta ve bu sistemler gelişen teknoloji ve algoritmalar sayesinde her geçen gün ilerleme kat etmektedir. Çalışmanın amacı, tıbbi görüntülemede yapay zekâ kullanımı konusunda tüm bileşenlerin ele alınarak bilgi verilmesi ve bu alanda çalışma yapacak araştırmacılara bir temel teşkil edecek bir alt yapı oluşturmaktır. Bunun sağlanması için yapay zekâ ve tıbbi görüntüleme konusu öncelikle ayrı bir şekilde ele alınmış, tıbbi görüntüleme teknolojileri kapsamlı bir şekilde anlatılmış ve tıbbi görüntülemede yapay zekâ kullanımının mevcut durumu, geleceği, sorunları ve çözümleri açık bir şekilde belirtilmiştir. Son olarak yapay zekâ teknikleri ile tıbbi görüntülerin işlenmesine dair çalışmalar verilerek çalışmanın teorik anlam bütünlüğü sağlanmıştır.

Keywords

Tıbbi Görüntüleme Teknolojileri, Yapay Zekâ, Bilgisayar Destekli Tanı Sistemleri

References

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An Overview of Artificial Intelligence and Medical Imaging Technologies

Year 2021, Volume: 5 Issue: 1, 207 - 230, 29.06.2021

Furkan Atlan İhsan Pençe

Abstract

Nowadays, the use of artificial intelligence is increasing steadily, particularly in the health sector. Deep learning, which is a sub-branch of artificial intelligence, is frequently preferred in the processing and interpretation of medical images, because it provides fruitful outcomes in image processing. Despite the development in medical imaging technologies and the increasing accuracy rate of disease diagnosis, accurate interpretation of these images by experts is time consuming, and unfavorable conditions may arise during treatment. For this reason, automated diagnostic systems are created using artificial intelligence, and these systems are improving gradually, owing to the evolution of several technologies and algorithms. This study aimed to provide information on the use of artificial intelligence in medical imaging with due consideration of all factors and create a base infrastructure for researchers in this field. To achieve this, previously artificial intelligence and medical imaging were discussed separately, placing more emphasis on medical imaging technologies. However, at present, potential problems and solutions in the use of artificial intelligence in medical imaging are clearly stated. In conclusion, by conducting more studies on the processing of medical images using artificial intelligence, the theoretical integrity of this field will become possible.

Keywords

Medical imaging technologies, Artificial intelligence, Computer-aided diagnosis systems

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Cheng, H. D., Cai, X., Chen, X., Hu, L., & Lou, X. (2003). Computer-aided detection and classification of microcalcifications in mammograms: a survey. Pattern recognition, 36(12), 2967-2991.
Chow, J. C., Boyd, S. K., Lichti, D. D., & Ronsky, J. L. (2020). Robust Self-Supervised Learning of Deterministic Errors in Single-Plane (Monoplanar) and Dual-Plane (Biplanar) X-ray Fluoroscopy. IEEE Transactions on Medical Imaging, 39(6), 2051-2060.
Coşkun, Y. (2019). Ayrık dalgacık dönüşümü tabanlı paralel görüntü sıkıştırma sistemi tasarımı (Master’s thesis, Maltepe Üniversitesi, Fen Bilimleri Enstitüsü). Cover, T., & Hart, P. (1967). Nearest neighbor pattern classification. IEEE transactions on information theory, 13(1), 21-27.
Çelik, G., & Talu, M. F. (2019). Çekişmeli üretken ağ modellerinin görüntü üretme performanslarının incelenmesi. Balıkesir Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 22(1), 181-192.
Dandıl, E., Seri̇n, Z. (2020). Derin Sinir Ağları Kullanarak Histopatolojik Görüntülerde Meme Kanseri Tespiti . Avrupa Bilim ve Teknoloji Dergisi , Ejosat Özel Sayı 2020 (HORA): 451-463.
Deng, J., Berg, A., Satheesh, S., Su, H., Khosla, A., & Fei-Fei, L. (2012). Imagenet large scale visual recognition competition 2012 (ILSVRC2012).
Doi, K. (2007). Computer-aided diagnosis in medical imaging: historical review, current status and future potential. Computerized medical imaging and graphics, 31(4-5), 198-211.
Dong, X., Lei, Y., Wang, T., Higgins, K., Liu, T., Curran, W. J., ... & Yang, X. (2020). Deep learning-based attenuation correction in the absence of structural information for whole-body positron emission tomography imaging. Physics in Medicine & Biology, 65(5), 055011. Düzleştirme Katmanı ile görüntü matrislerinin vektör hâline getirilmesi. (2021, 14 Ocak). Erişim Adresi: https://medium.com/@tuncerergin/ convolutional-neural-network-convnet-yada-cnn-nedir-nasil-calisir-97a0f5d34cad
Ehrlich, Ruth Ann., Daly, Joan A. (2008). Patient Care in Radiography: With an introduction to medical imaging (Seventh Edition). MO: Elsevier. E
l-Baz, A. S., & Suri, J. S. (2019). Lung imaging and CADx. Boca Raton, FL: CRC Press/Taylor & Francis.
El-Baz, A., Beache, G. M., Gimel’farb, G., Suzuki, K., Okada, K., Elnakib, A., ... & Abdollahi, B. (2013). Computer-aided diagnosis systems for lung cancer: challenges and methodologies. International journal of biomedical imaging.
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Details

Primary Language	Turkish
Subjects	Computer Software
Journal Section	Review
Authors	Furkan Atlan 0000-0003-1602-1941 İhsan Pençe 0000-0003-0734-3869
Publication Date	June 29, 2021
Submission Date	October 20, 2020
Published in Issue	Year 2021 Volume: 5 Issue: 1

Cite

APA	Atlan, F., & Pençe, İ. (2021). Yapay Zekâ ve Tıbbi Görüntüleme Teknolojilerine Genel Bakış. Acta Infologica, 5(1), 207-230.
AMA	Atlan F, Pençe İ. Yapay Zekâ ve Tıbbi Görüntüleme Teknolojilerine Genel Bakış. ACIN. June 2021;5(1):207-230.
Chicago	Atlan, Furkan, and İhsan Pençe. “Yapay Zekâ Ve Tıbbi Görüntüleme Teknolojilerine Genel Bakış”. Acta Infologica 5, no. 1 (June 2021): 207-30.
EndNote	Atlan F, Pençe İ (June 1, 2021) Yapay Zekâ ve Tıbbi Görüntüleme Teknolojilerine Genel Bakış. Acta Infologica 5 1 207–230.
IEEE	F. Atlan and İ. Pençe, “Yapay Zekâ ve Tıbbi Görüntüleme Teknolojilerine Genel Bakış”, ACIN, vol. 5, no. 1, pp. 207–230, 2021.
ISNAD	Atlan, Furkan - Pençe, İhsan. “Yapay Zekâ Ve Tıbbi Görüntüleme Teknolojilerine Genel Bakış”. Acta Infologica 5/1 (June 2021), 207-230.
JAMA	Atlan F, Pençe İ. Yapay Zekâ ve Tıbbi Görüntüleme Teknolojilerine Genel Bakış. ACIN. 2021;5:207–230.
MLA	Atlan, Furkan and İhsan Pençe. “Yapay Zekâ Ve Tıbbi Görüntüleme Teknolojilerine Genel Bakış”. Acta Infologica, vol. 5, no. 1, 2021, pp. 207-30.
Vancouver	Atlan F, Pençe İ. Yapay Zekâ ve Tıbbi Görüntüleme Teknolojilerine Genel Bakış. ACIN. 2021;5(1):207-30.

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