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

3 Boyutlu Mamogram Görüntüsü. WAKE RADIOLOGY. (2020, 8 Haziran). Erişim Adresi: https://www.wakerad.com/whats-new/ study-3-d-scans-accurate-standard-mammograms/
ABD’li ve Çinli şirketler Yapay Zekaya hükmetmek için yarışıyor. (2020, 17 Ekim). Erişim Adresi: https://www.wsj.com/articles/ why-u-s-companies-may-losethe-ai-race-1516280677
Alp, H., Akıncı, T. Ç., & Albora, M. (2008). Jeofizik Uygulamalarda Fourier ve Dalgacık Dönüşümlerinin Karşılaştırılması. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 14(1), 67-76.
Al-shamasneh, A. R. M., & Obaidellah, U. H. B. (2017). Artificial intelligence techniques for cancer detection and classification: review study. European Scientific Journal, 13(3), 342-370. Arslan, T. X Işınları ve Kullanım Alanları. Gazi Üniversitesi Tezi, 2010.
Badrinarayanan, V., Kendall, A., & Cipolla, R. (2017). Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence, 39(12), 2481-2495.
Bailey, D. L., Maisey, M. N., Townsend, D. W., & Valk, P. E. (2005). Positron emission tomography (Vol. 2). London: Springer. Bankman, I. N. (2009). Handbook of medical image processing and analysis. Boston: Academic Press.
Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R. L., Torre, A., & Jemal, A. (2018). Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: a cancer journal for clinicians, vol. 6, no. 8, pp. 394-424.
Buck, A. K., Nekolla, S., Ziegler, S., Beer, A., Krause, B. J., Herrmann, K., ... & Drzezga, A. (2008). Spect/ct. Journal of Nuclear Medicine, 49(8), 1305-1319.
Budak, Ü. (2019). SegNet Mimarisi ile Bilgisayarlı Tomografi Görüntülerinden Karaciğer Bölgesinin Bölütlenmesi. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 31(1), 215-222.
Bushberg, J. T., Seibert, J. A., Leidholdt Jr, E. M., Boone, J. M., & Goldschmidt Jr, E. J. (2011). Introduction to Medical Imaging. The essential physics of medical imaging. 3 rd edition: Lippincott Williams & Wilkins.
Buxton, R. B. (2009). Introduction to Functional Magnetic Resonance Imaging Principles and Techniques. Cambridge: Cambridge University Press.
Cai, C., Wang, C., Zeng, Y., Cai, S., Liang, D., Wu, Y., Chen, Z., Ding, X. and Zhong, J. (2018). Single‐shot T2 mapping using overlapping‐echo detachment planar imaging and a deep convolutional neural network. Magnetic resonance in medicine, 80(5), 2202-2214.
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.
Ertel, W., Black, N., & Mast, F. (2017). Introduction to artificial intelligence. Cham, Switzerland: Springer. European Society of Radiology (ESR. (2015). ESR position paper on imaging biobanks. Insights into imaging, 6(4), 403-410.
Floroskopi Prosedürü ve Görüntüsü, Angela Betsaida B. Laguipo. (2020, 8 Haziran). Erişim Adresi: https://www.news-medical.net/health/Fluoroscopy- Procedure.aspx
Fotin, S. V., Yin, Y., Haldankar, H., Hoffmeister, J. W., & Periaswamy, S. (2016, March). Detection of soft tissue densities from digital breast tomosynthesis: comparison of conventional and deep learning approaches. In Medical Imaging 2016: Computer-Aided Diagnosis (Vol. 9785, p. 97850X). International Society for Optics and Photonics.
Ghoneim, A., Muhammad, G., & Hossain, M. S. (2020). Cervical cancer classification using convolutional neural networks and extreme learning machines. Future Generation Computer Systems. 102: 643-649.
Gonzalez, Rafael (2018). Digital image processing. New York, NY: Pearson.
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., ... & Bengio, Y. (2014). Generative adversarial nets. Advances in neural information processing systems, 27, 2672-2680.
Greenspan, H., Van Ginneken, B., & Summers, R. M. (2016). Guest editorial deep learning in medical imaging: Overview and future promise of an exciting new technique. IEEE Transactions on Medical Imaging, 35(5), 1153-1159.
Gulli, A., Kapoor, A., & Pal, S. (2019). Deep learning with TensorFlow 2 and Keras: Regression, ConvNets, GANs, RNNs, NLP, and more with TensorFlow 2 and the Keras API. Birmingham: Packt Publishing.
Haidekker, M. A. (2013). Medical Imaging Technology. New York, NY: Springer.
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).
Hou, W., Zhang, D., Wei, Y., Guo, J., & Zhang, X. (2020). Review on Computer Aided Weld Defect Detection from Radiography Images. Applied Sciences, 10(5), 1878.
Huettel, S. A., Song, A. W., & McCarthy, G. (2004). Functional magnetic resonance imaging (Vol. 1). Sunderland, MA: Sinauer Associates.
Hutton, L., & Henderson, T. (2017). Beyond the EULA: Improving consent for data mining. In Transparent Data Mining for Big and Small Data (pp. 147-167). Cham: Springer International Publishing.
ILSVRC2012. Large Scale Visual Recognition Challenge 2012. (2020, 22 Aralık). Erişim Adresi: https://www.kdnuggets.com/2018/12/deep-learningmajor- advances-review.html Imagenet’te Görüntü Sınıflandırması. (2020, 6 Eylül). Erişim Adresi: https://paperswithcode.com/sota/image-classification-on-imagenet
Kiani, A., Uyumazturk, B., Rajpurkar, P., Wang, A., Gao, R., Jones, E., ... & Martin, B. A. (2020). Impact of a deep learning assistant on the histopathologic classification of liver cancer. NPJ digital medicine. 3(1): 1-8.
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems. 1097-1105.
Kubat, M. (2018). Introductıon To Machıne Learnıng. Place of publication not identified: SPRINGER INTERNATIONAL PU.
Lameka, K., Farwell, M. D., & Ichise, M. (2016). Positron emission tomography. In Handbook of clinical neurology (Vol. 135, pp. 209-227). Amsterdam: Elsevier.
Larson, C., Lionhart, P., Roh, A., & Colglazier, R. (2018). Introduction to fluoroscopy: For residents & professionals alike. Place of publication not identified: Prometheus Liionhart.
LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324. Leighton, T. G. (2007). What is ultrasound? Progress in biophysics and molecular biology, 93(1-3), 3-83.
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., ... & Sánchez, C. I. (2017). A survey on deep learning in medical image analysis. Medical image analysis, 42, 60-88.
Maulik, D., & Zalud, I. (Eds.). (2005). Doppler ultrasound in obstetrics and gynecology (pp. 363-374). Berlin: Springer.
Mettler, F. A., & Guiberteau, M. J. (2012). Essentials of nuclear medicine imaging. Philadelphia, PA: Elsevier/Saunders.
Morra, L., Delsanto, S., & Correale, L. (2019). Artificial Intelligence in Medical Imaging: From Theory to Clinical Practice. Boca Raton: CRC Press.
Murino, V., Puppo, E., Sona, D., Cristani, M., & Sansone, C. (Eds.). (2015). New Trends in Image Analysis and Processing--ICIAP 2015 Workshops: ICIAP 2015 International Workshops, BioFor, CTMR, RHEUMA, ISCA, MADiMa, SBMI, and QoEM, Genoa, Italy, September 7-8, 2015, Proceedings (Vol. 9281). Heidelberg: Springer.
Neri, E., & Regge, D. (2017). Imaging biobanks in oncology: European perspective. Future Oncology, 13(5), 433-441. O r t a k l a m a k a t m a n ı t ü r l e r i . ( 2 0 2 1 , 1 4 O c a k ) . E r i ş i m A d r e s i : h t t p s : / / t o w a r d s d a t a s c i e n c e . com/a-comprehensive-guide-to-convolutional-neural-networks-the-eli5-way-3bd2b1164a53
Öner, İ. V., Yeşilyurt, M. K., & Yılmaz, E. Ç. (2017). WAVELET ANALİZ TEKNİĞİ VE UYGULAMA ALANLARI. Ordu Üniversitesi Bilim ve Teknoloji Dergisi, 7(1), 42-56.
Öztad, E. (2020). Meme Kanseri Tespitinde Sınıflandırma ve Sinir Ağları Yöntemlerinin Karşılaştırılması. İleri Mühendislik Çalışmaları ve Teknolojileri Dergisi. 1(1): 49-54.
Öztemel, E. (2006). Yapay Sinir Ağları. 2. Baskı. Papatya Yayıncılık: İstanbul.
Pisano, E. D., & Yaffe, M. J. Digital mammography. Radiology, vol. 234, no. 2, pp. 353-362, 2005.
Ranschaert, E. R., Morozov, S., & Algra, P. R. (2019). Artificial Intelligence in Medical Imaging: Opportunities, Applications and Risks. Cham: Springer International Publishing.
Ronneberger, O., Fischer, P., & Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention (pp. 234-241). Springer, Cham.
Sağlık hizmetleri verileri ve gevşek kurallar Çin’in Yapay Zekâ konusunda başarılı olmasına nasıl yardımcı oluyor? (2020, 17 Ekim). Erişim Adresi: https://www.wired.com/story/health-care-data-laxrules-help-china-prosper-ai/
Sahiner, B., Pezeshk, A., Hadjiiski, L. M., Wang, X., Drukker, K., Cha, K. H., ... & Giger, M. L. (2019). Deep learning in medical imaging and radiation therapy. Medical physics, 46(1), e1-e36.
Sarhan, A. M. (2020). Brain Tumor Classification in Magnetic Resonance Images Using Deep Learning and Wavelet Transform. Journal of Biomedical Science and Engineering, 13(06), 102.
Sato, E., Nakayama, K., Nakamura, K., Ishikawa, M., Katagiri, H., & Kyo, S. (2015). A case with life-threatening uterine bleeding due to postmenopausal uterine arteriovenous malformation. BMC Women’s Health, 15(1), 1-5.
Shi, L., Onofrey, J. A., Liu, H., Liu, Y. H., & Liu, C. (2020). Deep learning-based attenuation map generation for myocardial perfusion SPECT. European Journal of Nuclear Medicine and Molecular Imaging, 1-13.
Shia, W., & Chen, D. (2020). Abstract P1-02-10: Using deep residual networks for malignant and benign classification of two-dimensional Doppler breast ultrasound imaging.
Shung, K. K. (2015). Diagnostic ultrasound: Imaging and blood flow measurements. Boca Raton, BR: CRC press.
Sluimer, I., Schilham, A., Prokop, M., & Van Ginneken, B. (2006). Computer analysis of computed tomography scans of the lung: a survey. IEEE transactions on medical imaging, 25(4), 385-405.
Smith, N. & Webb, A. (2010). Nuclear medicine: Planar scintigraphy, SPECT and PET/CT. In Introduction to Medical Imaging: Physics, Engineering and Clinical Applications (Cambridge Texts in Biomedical Engineering, pp. 89-144). Cambridge: Cambridge University Press. doi:10.1017/ CBO9780511760976.003
SPECT görüntüleri ile Alzheimer Hastalığı Teşhisi. (2020, 10 Haziran). Erişim Adresi: https://www.pinterest.co.uk/pin/350717889706920247/
Szabo, T.L. Doppler Models. (2004). Diagnostic ultrasound imaging: inside out. 2 nd edition: Academic Press.
Talo, M. (2019, April). Pneumonia Detection from Radiography Images using Convolutional Neural Networks. In 2019 27th Signal Processing and Communications Applications Conference (SIU) (pp. 1-4). IEEE.
The Economist. “Çalışmamaktan Sinir Ağına”. (2020, 3 Eylül). Erişim Adresi: https://www.economist.com/special-report/2016/06/23/ from-not-working-to-neural-networking
The, L. (2018). Artificial intelligence in health care: within touching distance. Lancet (London, England), 390(10114), 2739.
Ti̇ryaki̇, V. (2020). Mamografi görüntülerindeki anormalliklerin yerel ikili örüntü ve varyantları kullanılarak sınıflandırılması. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi , 9 (1) , 297-305 . DOI: 10.17798/bitlisfen.557411.
Vakanski, A., Xian, M., & Freer, P. E. (2020). Attention-Enriched Deep Learning Model for Breast Tumor Segmentation in Ultrasound Images. Ultrasound in Medicine & Biology, 46(10), 2819-2833.
Wang, H., & Raj, B. (2017). On the origin of deep learning. arXiv preprint arXiv:1702.07800.
Webb, S., & Flower, M. A. (2016). Webb’s physics of medical imaging. Place of publication not identified: CRC Press. Wilhelm Conrad Röntgen’in eşine ait ilk radyografi görüntüsü. (2020, 8 Ekim). Erişim Adresi: https://www.winally.com/2018/09/ yeni-nesil-goruntuleme/
Wu, X., Xu, K., & Hall, P. (2017). A survey of image synthesis and editing with generative adversarial networks. Tsinghua Science and Technology, 22(6), 660-674.
Yapay Zekâ, Tıbbi Teşhise karşı. Teşhis otomatikleştirildiğinde ne olur? (2020, 15 Eylül). Erişim Adresi:https://www.newyorker.com/magazine/2017/04/03/ ai-versus-md Zekâ teriminin tanımı.
Türk Dil Kurumu. (10 Eylül, 2020). Erişim Adresi: https://www.sozluk.gov.tr (Erişim zamanı: 10.10.2020).

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

References

3 Boyutlu Mamogram Görüntüsü. WAKE RADIOLOGY. (2020, 8 Haziran). Erişim Adresi: https://www.wakerad.com/whats-new/ study-3-d-scans-accurate-standard-mammograms/
ABD’li ve Çinli şirketler Yapay Zekaya hükmetmek için yarışıyor. (2020, 17 Ekim). Erişim Adresi: https://www.wsj.com/articles/ why-u-s-companies-may-losethe-ai-race-1516280677
Alp, H., Akıncı, T. Ç., & Albora, M. (2008). Jeofizik Uygulamalarda Fourier ve Dalgacık Dönüşümlerinin Karşılaştırılması. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 14(1), 67-76.
Al-shamasneh, A. R. M., & Obaidellah, U. H. B. (2017). Artificial intelligence techniques for cancer detection and classification: review study. European Scientific Journal, 13(3), 342-370. Arslan, T. X Işınları ve Kullanım Alanları. Gazi Üniversitesi Tezi, 2010.
Badrinarayanan, V., Kendall, A., & Cipolla, R. (2017). Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence, 39(12), 2481-2495.
Bailey, D. L., Maisey, M. N., Townsend, D. W., & Valk, P. E. (2005). Positron emission tomography (Vol. 2). London: Springer. Bankman, I. N. (2009). Handbook of medical image processing and analysis. Boston: Academic Press.
Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R. L., Torre, A., & Jemal, A. (2018). Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: a cancer journal for clinicians, vol. 6, no. 8, pp. 394-424.
Buck, A. K., Nekolla, S., Ziegler, S., Beer, A., Krause, B. J., Herrmann, K., ... & Drzezga, A. (2008). Spect/ct. Journal of Nuclear Medicine, 49(8), 1305-1319.
Budak, Ü. (2019). SegNet Mimarisi ile Bilgisayarlı Tomografi Görüntülerinden Karaciğer Bölgesinin Bölütlenmesi. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 31(1), 215-222.
Bushberg, J. T., Seibert, J. A., Leidholdt Jr, E. M., Boone, J. M., & Goldschmidt Jr, E. J. (2011). Introduction to Medical Imaging. The essential physics of medical imaging. 3 rd edition: Lippincott Williams & Wilkins.
Buxton, R. B. (2009). Introduction to Functional Magnetic Resonance Imaging Principles and Techniques. Cambridge: Cambridge University Press.
Cai, C., Wang, C., Zeng, Y., Cai, S., Liang, D., Wu, Y., Chen, Z., Ding, X. and Zhong, J. (2018). Single‐shot T2 mapping using overlapping‐echo detachment planar imaging and a deep convolutional neural network. Magnetic resonance in medicine, 80(5), 2202-2214.
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.
Ertel, W., Black, N., & Mast, F. (2017). Introduction to artificial intelligence. Cham, Switzerland: Springer. European Society of Radiology (ESR. (2015). ESR position paper on imaging biobanks. Insights into imaging, 6(4), 403-410.
Floroskopi Prosedürü ve Görüntüsü, Angela Betsaida B. Laguipo. (2020, 8 Haziran). Erişim Adresi: https://www.news-medical.net/health/Fluoroscopy- Procedure.aspx
Fotin, S. V., Yin, Y., Haldankar, H., Hoffmeister, J. W., & Periaswamy, S. (2016, March). Detection of soft tissue densities from digital breast tomosynthesis: comparison of conventional and deep learning approaches. In Medical Imaging 2016: Computer-Aided Diagnosis (Vol. 9785, p. 97850X). International Society for Optics and Photonics.
Ghoneim, A., Muhammad, G., & Hossain, M. S. (2020). Cervical cancer classification using convolutional neural networks and extreme learning machines. Future Generation Computer Systems. 102: 643-649.
Gonzalez, Rafael (2018). Digital image processing. New York, NY: Pearson.
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., ... & Bengio, Y. (2014). Generative adversarial nets. Advances in neural information processing systems, 27, 2672-2680.
Greenspan, H., Van Ginneken, B., & Summers, R. M. (2016). Guest editorial deep learning in medical imaging: Overview and future promise of an exciting new technique. IEEE Transactions on Medical Imaging, 35(5), 1153-1159.
Gulli, A., Kapoor, A., & Pal, S. (2019). Deep learning with TensorFlow 2 and Keras: Regression, ConvNets, GANs, RNNs, NLP, and more with TensorFlow 2 and the Keras API. Birmingham: Packt Publishing.
Haidekker, M. A. (2013). Medical Imaging Technology. New York, NY: Springer.
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).
Hou, W., Zhang, D., Wei, Y., Guo, J., & Zhang, X. (2020). Review on Computer Aided Weld Defect Detection from Radiography Images. Applied Sciences, 10(5), 1878.
Huettel, S. A., Song, A. W., & McCarthy, G. (2004). Functional magnetic resonance imaging (Vol. 1). Sunderland, MA: Sinauer Associates.
Hutton, L., & Henderson, T. (2017). Beyond the EULA: Improving consent for data mining. In Transparent Data Mining for Big and Small Data (pp. 147-167). Cham: Springer International Publishing.
ILSVRC2012. Large Scale Visual Recognition Challenge 2012. (2020, 22 Aralık). Erişim Adresi: https://www.kdnuggets.com/2018/12/deep-learningmajor- advances-review.html Imagenet’te Görüntü Sınıflandırması. (2020, 6 Eylül). Erişim Adresi: https://paperswithcode.com/sota/image-classification-on-imagenet
Kiani, A., Uyumazturk, B., Rajpurkar, P., Wang, A., Gao, R., Jones, E., ... & Martin, B. A. (2020). Impact of a deep learning assistant on the histopathologic classification of liver cancer. NPJ digital medicine. 3(1): 1-8.
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems. 1097-1105.
Kubat, M. (2018). Introductıon To Machıne Learnıng. Place of publication not identified: SPRINGER INTERNATIONAL PU.
Lameka, K., Farwell, M. D., & Ichise, M. (2016). Positron emission tomography. In Handbook of clinical neurology (Vol. 135, pp. 209-227). Amsterdam: Elsevier.
Larson, C., Lionhart, P., Roh, A., & Colglazier, R. (2018). Introduction to fluoroscopy: For residents & professionals alike. Place of publication not identified: Prometheus Liionhart.
LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324. Leighton, T. G. (2007). What is ultrasound? Progress in biophysics and molecular biology, 93(1-3), 3-83.
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., ... & Sánchez, C. I. (2017). A survey on deep learning in medical image analysis. Medical image analysis, 42, 60-88.
Maulik, D., & Zalud, I. (Eds.). (2005). Doppler ultrasound in obstetrics and gynecology (pp. 363-374). Berlin: Springer.
Mettler, F. A., & Guiberteau, M. J. (2012). Essentials of nuclear medicine imaging. Philadelphia, PA: Elsevier/Saunders.
Morra, L., Delsanto, S., & Correale, L. (2019). Artificial Intelligence in Medical Imaging: From Theory to Clinical Practice. Boca Raton: CRC Press.
Murino, V., Puppo, E., Sona, D., Cristani, M., & Sansone, C. (Eds.). (2015). New Trends in Image Analysis and Processing--ICIAP 2015 Workshops: ICIAP 2015 International Workshops, BioFor, CTMR, RHEUMA, ISCA, MADiMa, SBMI, and QoEM, Genoa, Italy, September 7-8, 2015, Proceedings (Vol. 9281). Heidelberg: Springer.
Neri, E., & Regge, D. (2017). Imaging biobanks in oncology: European perspective. Future Oncology, 13(5), 433-441. O r t a k l a m a k a t m a n ı t ü r l e r i . ( 2 0 2 1 , 1 4 O c a k ) . E r i ş i m A d r e s i : h t t p s : / / t o w a r d s d a t a s c i e n c e . com/a-comprehensive-guide-to-convolutional-neural-networks-the-eli5-way-3bd2b1164a53
Öner, İ. V., Yeşilyurt, M. K., & Yılmaz, E. Ç. (2017). WAVELET ANALİZ TEKNİĞİ VE UYGULAMA ALANLARI. Ordu Üniversitesi Bilim ve Teknoloji Dergisi, 7(1), 42-56.
Öztad, E. (2020). Meme Kanseri Tespitinde Sınıflandırma ve Sinir Ağları Yöntemlerinin Karşılaştırılması. İleri Mühendislik Çalışmaları ve Teknolojileri Dergisi. 1(1): 49-54.
Öztemel, E. (2006). Yapay Sinir Ağları. 2. Baskı. Papatya Yayıncılık: İstanbul.
Pisano, E. D., & Yaffe, M. J. Digital mammography. Radiology, vol. 234, no. 2, pp. 353-362, 2005.
Ranschaert, E. R., Morozov, S., & Algra, P. R. (2019). Artificial Intelligence in Medical Imaging: Opportunities, Applications and Risks. Cham: Springer International Publishing.
Ronneberger, O., Fischer, P., & Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention (pp. 234-241). Springer, Cham.
Sağlık hizmetleri verileri ve gevşek kurallar Çin’in Yapay Zekâ konusunda başarılı olmasına nasıl yardımcı oluyor? (2020, 17 Ekim). Erişim Adresi: https://www.wired.com/story/health-care-data-laxrules-help-china-prosper-ai/
Sahiner, B., Pezeshk, A., Hadjiiski, L. M., Wang, X., Drukker, K., Cha, K. H., ... & Giger, M. L. (2019). Deep learning in medical imaging and radiation therapy. Medical physics, 46(1), e1-e36.
Sarhan, A. M. (2020). Brain Tumor Classification in Magnetic Resonance Images Using Deep Learning and Wavelet Transform. Journal of Biomedical Science and Engineering, 13(06), 102.
Sato, E., Nakayama, K., Nakamura, K., Ishikawa, M., Katagiri, H., & Kyo, S. (2015). A case with life-threatening uterine bleeding due to postmenopausal uterine arteriovenous malformation. BMC Women’s Health, 15(1), 1-5.
Shi, L., Onofrey, J. A., Liu, H., Liu, Y. H., & Liu, C. (2020). Deep learning-based attenuation map generation for myocardial perfusion SPECT. European Journal of Nuclear Medicine and Molecular Imaging, 1-13.
Shia, W., & Chen, D. (2020). Abstract P1-02-10: Using deep residual networks for malignant and benign classification of two-dimensional Doppler breast ultrasound imaging.
Shung, K. K. (2015). Diagnostic ultrasound: Imaging and blood flow measurements. Boca Raton, BR: CRC press.
Sluimer, I., Schilham, A., Prokop, M., & Van Ginneken, B. (2006). Computer analysis of computed tomography scans of the lung: a survey. IEEE transactions on medical imaging, 25(4), 385-405.
Smith, N. & Webb, A. (2010). Nuclear medicine: Planar scintigraphy, SPECT and PET/CT. In Introduction to Medical Imaging: Physics, Engineering and Clinical Applications (Cambridge Texts in Biomedical Engineering, pp. 89-144). Cambridge: Cambridge University Press. doi:10.1017/ CBO9780511760976.003
SPECT görüntüleri ile Alzheimer Hastalığı Teşhisi. (2020, 10 Haziran). Erişim Adresi: https://www.pinterest.co.uk/pin/350717889706920247/
Szabo, T.L. Doppler Models. (2004). Diagnostic ultrasound imaging: inside out. 2 nd edition: Academic Press.
Talo, M. (2019, April). Pneumonia Detection from Radiography Images using Convolutional Neural Networks. In 2019 27th Signal Processing and Communications Applications Conference (SIU) (pp. 1-4). IEEE.
The Economist. “Çalışmamaktan Sinir Ağına”. (2020, 3 Eylül). Erişim Adresi: https://www.economist.com/special-report/2016/06/23/ from-not-working-to-neural-networking
The, L. (2018). Artificial intelligence in health care: within touching distance. Lancet (London, England), 390(10114), 2739.
Ti̇ryaki̇, V. (2020). Mamografi görüntülerindeki anormalliklerin yerel ikili örüntü ve varyantları kullanılarak sınıflandırılması. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi , 9 (1) , 297-305 . DOI: 10.17798/bitlisfen.557411.
Vakanski, A., Xian, M., & Freer, P. E. (2020). Attention-Enriched Deep Learning Model for Breast Tumor Segmentation in Ultrasound Images. Ultrasound in Medicine & Biology, 46(10), 2819-2833.
Wang, H., & Raj, B. (2017). On the origin of deep learning. arXiv preprint arXiv:1702.07800.
Webb, S., & Flower, M. A. (2016). Webb’s physics of medical imaging. Place of publication not identified: CRC Press. Wilhelm Conrad Röntgen’in eşine ait ilk radyografi görüntüsü. (2020, 8 Ekim). Erişim Adresi: https://www.winally.com/2018/09/ yeni-nesil-goruntuleme/
Wu, X., Xu, K., & Hall, P. (2017). A survey of image synthesis and editing with generative adversarial networks. Tsinghua Science and Technology, 22(6), 660-674.
Yapay Zekâ, Tıbbi Teşhise karşı. Teşhis otomatikleştirildiğinde ne olur? (2020, 15 Eylül). Erişim Adresi:https://www.newyorker.com/magazine/2017/04/03/ ai-versus-md Zekâ teriminin tanımı.
Türk Dil Kurumu. (10 Eylül, 2020). Erişim Adresi: https://www.sozluk.gov.tr (Erişim zamanı: 10.10.2020).

There are 76 citations in total.

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.

Download Cover Image

Article Files

Full Text