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Deep Learning Applications in Medical Image Processing

Yıl 2021, Cilt: 5 Sayı: 2, 459 - 474, 30.12.2021
https://doi.org/10.26650/acin.927561

Öz

Medical imaging techniques such as magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), mammography, ultrasound and x-ray have been used for the diagnosis and treatment of diseases for many years. However, machine learning methods are used in this field for reasons such as earlier diagnosis of diseases, reduce the workload of doctors, and adjudicate conflicting expert opinions. With the increase in the amount of data, machine learning methods have remained insufficient in the field of image processing. Thanks to the developing mathematical models and hardware devices, deep learning has taken a wide place in this field. In this study, the application of deep learning methods in the field of medical image processing has been examined. Very recent examples are presented from studies in the fields of segmentation, classification and disease diagnosis, image generation, image transformation and image enhancement. The algorithms used in the studies are briefly explained. In addition, brain tumor segmentation with deep learning was tried on the BraTS 2020 dataset, and as a result, a dice similarity rate of 86% and a sensitivity value of 80% were obtained. Our aim is for this study to guide different studies on medical images with deep learning methods and serve as a basic resource in this field.

Kaynakça

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Medikal Görüntü İşlemede Derin Öğrenme Uygulamaları

Yıl 2021, Cilt: 5 Sayı: 2, 459 - 474, 30.12.2021
https://doi.org/10.26650/acin.927561

Öz

Manyetik rezonans görüntüleme (MRI), bilgisayarlı tomografi (BT), pozitron emisyon tomografisi (PET), mamografi, ultrason ve röntgen gibi tıbbi görüntüleme teknikleri uzun yıllardan beri hastalıkların teşhisi, tanısı ve tedavisi için kullanılmıştır. Ancak hastalıkların daha erken teşhisi, uzmanların yoğunluğunu azaltma, çakışan uzman görüşlerini karara bağlama gibi nedenlerle, bu alanda makine öğrenmesi yöntemlerinden yararlanılmaktadır. Veri miktarının artması ile makine öğrenmesi yöntemleri görüntü işleme alanında yetersiz kalmış, gelişen matematiksel modeller ve donanımsal cihazlar sayesinde derin öğrenme bu alanda kendine geniş bir yer edinmiştir. Bu çalışmada derin öğrenme yöntemlerinin medikal görüntü işleme alanında uygulanması incelenmiştir. Segmentasyon, sınıflandırma ve hastalık teşhisi, görüntü oluşturma, dönüştürme ve iyileştirme alanlarında yapılan çalışmalardan oldukça güncel örnekler sunulmuş, yapılan çalışmalarda kullanılan algoritmalar kısaca açıklanmıştır. Ayrıca BraTS 2020 veri seti üzerinde derin öğrenme ile beyin tümör segmentasyonu gerçekleştirme denenmiş, sonuç olarak %86 dice benzerlik oranı ve %80 hassasiyet değeri elde edilmiştir. Bu çalışmanın medikal görüntüler üzerinde derin öğrenme yöntemleri ile yapılacak farklı çalışmalara yol gösterecek bir kaynak olması hedeflenmiştir.

Kaynakça

  • Alom, M. Z., Yakopcic, C., Hasan, M., Taha, T. M., & Asari, V. K. (2019). Recurrent residual U-Net for medical image segmentation. Journal of Medical Imaging, 6(1), 014006.
  • Alyafeai, Z., & Ghouti, L. (2020). A fully-automated deep learning pipeline for cervical cancer classification. Expert Systems with Applications, 141, 112951.
  • Angelov, P., & Almeida Soares, E. (2020). SARS-CoV-2 CT-scan dataset: A large dataset of real patients CT scans for SARS-CoV-2 identification. MedRxiv.
  • Armanious, K., Jiang, C., Fischer, M., Küstner, T., Hepp, T., Nikolaou, K., ... & Yang, B. (2020). MedGAN: Medical image translation using GANs. Computerized Medical Imaging and Graphics, 79, 101684.
  • Arvidsson, I., Overgaard, N. C., Marginean, F. E., Krzyzanowska, A., Bjartell, A., Åström, K., & Heyden, A. (2018, April). Generalization of prostate cancer classification for multiple sites using deep learning. In 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018) (pp. 191-194). IEEE.
  • Bakas, S., Reyes, M., Jakab, A., Bauer, S., Rempfler, M., Crimi, A., ... & Eaton-Rosen, Z. (2018). Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge. arXiv preprint arXiv:1811.02629.
  • Bera, S., & Biswas, P. K. (2021). Noise Conscious Training of Non Local Neural Network Powered by Self Attentive Spectral Normalized Markovian Patch GAN for Low Dose CT Denoising. IEEE Transactions on Medical Imaging.
  • Bowyer, K., Kopans, D., Kegelmeyer, W. P., Moore, R., Sallam, M., Chang, K., & Woods, K. (1996, June). The digital database for screening mammography. In Third international workshop on digital mammography (Vol. 58, p. 27).
  • Brosch, T., Tam, R., & Alzheimer’s Disease Neuroimaging Initiative. (2013, September). Manifold learning of brain MRIs by deep learning. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 633-640). Springer, Berlin, Heidelberg.
  • Chu, C., Zhmoginov, A., & Sandler, M. (2017). Cyclegan, a master of steganography. arXiv preprint arXiv:1712.02950. Cirillo, M. D., Abramian, D., & Eklund, A. (2020). Vox2Vox: 3D-GAN for brain tumour segmentation. arXiv preprint arXiv:2003.13653.
  • Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., ... & Prior, F. (2013). The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository. Journal of digital imaging, 26(6), 1045-1057.
  • Çiçek, Ö., Abdulkadir, A., Lienkamp, S. S., Brox, T., & Ronneberger, O. (2016, October). 3D U-Net: learning dense volumetric segmentation from sparse annotation. In International conference on medical image computing and computer-assisted intervention (pp. 424-432). Springer, Cham.
  • Díaz-Pernas, F. J., Martínez-Zarzuela, M., Antón-Rodríguez, M., & González-Ortega, D. (2021, February). A deep learning approach for brain tumor classification and segmentation using a multiscale convolutional neural network. In Healthcare (Vol. 9, No. 2, p. 153). Multidisciplinary Digital Publishing Institute.
  • Dorj, U. O., Lee, K. K., Choi, J. Y., & Lee, M. (2018). The skin cancer classification using deep convolutional neural network. Multimedia Tools and Applications, 77(8), 9909-9924.
  • El Kader Isselmou, A., Xu, G., Zhang, S., Saminu, S., & Javaid, I. (2019, July). Deep learning algorithm for brain tumor detection and analysis using MR brain images. In Proceedings of the 2019 International Conference on Intelligent Medicine and Health (pp. 28-32).
  • Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. nature, 542(7639), 115-118.
  • Farag, A., Lu, L., Roth, H. R., Liu, J., Turkbey, E., & Summers, R. M. (2017). Automatic pancreas segmentation using coarse-to-fine Superpixel labeling. In Deep learning and convolutional neural networks for medical image computing (pp. 279-302). Springer, Cham.
  • Farooq, M., & Hafeez, A. (2020). Covid-resnet: A deep learning framework for screening of covid19 from radiographs. arXiv preprint arXiv:2003.14395.
  • Gargeya, R., & Leng, T. (2017). Automated identification of diabetic retinopathy using deep learning. Ophthalmology, 124(7), 962-969.
  • Gómez-Valverde, J. J., Antón, A., Fatti, G., Liefers, B., Herranz, A., Santos, A., ... & Ledesma-Carbayo, M. J. (2019). Automatic glaucoma classification using color fundus images based on convolutional neural networks and transfer learning. Biomedical optics express, 10(2), 892-913.
  • 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.
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Toplam 81 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Bölüm Derleme
Yazarlar

Ayşe Gül Eker 0000-0003-0721-2631

Nevcihan Duru 0000-0003-2154-7067

Erken Görünüm Tarihi 13 Eylül 2021
Yayımlanma Tarihi 30 Aralık 2021
Gönderilme Tarihi 25 Nisan 2021
Yayımlandığı Sayı Yıl 2021 Cilt: 5 Sayı: 2

Kaynak Göster

APA Eker, A. G., & Duru, N. (2021). Medikal Görüntü İşlemede Derin Öğrenme Uygulamaları. Acta Infologica, 5(2), 459-474. https://doi.org/10.26650/acin.927561
AMA Eker AG, Duru N. Medikal Görüntü İşlemede Derin Öğrenme Uygulamaları. ACIN. Aralık 2021;5(2):459-474. doi:10.26650/acin.927561
Chicago Eker, Ayşe Gül, ve Nevcihan Duru. “Medikal Görüntü İşlemede Derin Öğrenme Uygulamaları”. Acta Infologica 5, sy. 2 (Aralık 2021): 459-74. https://doi.org/10.26650/acin.927561.
EndNote Eker AG, Duru N (01 Aralık 2021) Medikal Görüntü İşlemede Derin Öğrenme Uygulamaları. Acta Infologica 5 2 459–474.
IEEE A. G. Eker ve N. Duru, “Medikal Görüntü İşlemede Derin Öğrenme Uygulamaları”, ACIN, c. 5, sy. 2, ss. 459–474, 2021, doi: 10.26650/acin.927561.
ISNAD Eker, Ayşe Gül - Duru, Nevcihan. “Medikal Görüntü İşlemede Derin Öğrenme Uygulamaları”. Acta Infologica 5/2 (Aralık 2021), 459-474. https://doi.org/10.26650/acin.927561.
JAMA Eker AG, Duru N. Medikal Görüntü İşlemede Derin Öğrenme Uygulamaları. ACIN. 2021;5:459–474.
MLA Eker, Ayşe Gül ve Nevcihan Duru. “Medikal Görüntü İşlemede Derin Öğrenme Uygulamaları”. Acta Infologica, c. 5, sy. 2, 2021, ss. 459-74, doi:10.26650/acin.927561.
Vancouver Eker AG, Duru N. Medikal Görüntü İşlemede Derin Öğrenme Uygulamaları. ACIN. 2021;5(2):459-74.