Dergiler / The International Journal of Energy and Engineering Sciences / 2018 / Cilt: 3 - Sayı: 2

ON THE USE OF DEEP LEARNING METHODS ON MEDICAL IMAGES

Sayfa
1–15
Yayın Tarihi
2019-01-01
DOI
—
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

This study reviews literature studies of recent years that utilized Deep Learning algorithms on medical images in order to present a general picture of the relevant literature.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Deep Learning algorithms have recently been reported to be successful in the analysis of images and voice.
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Temel Bulgular & Veri:These algorithms, specifically Convolutional Neural Network (CNN), have also proven themselves to be highly promising on images produced by medical imaging technologies, as well.
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Sonuç & Katkı:This study reviews literature studies of recent years that utilized Deep Learning algorithms on medical images in order to present a general picture of the relevant literature.
🔬Tür:Literatür & Derleme Çalışması
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Deep Learning · medical images · CNN

Abstract

Deep Learning algorithms have recently been reported to be successful in the analysis of images and voice. These algorithms, specifically Convolutional Neural Network (CNN), have also proven themselves to be highly promising on images produced by medical imaging technologies, as well. By use of deep learning algorithms, researchers have accomplished several tasks in this field including image classification, object and lesion detection and segmentation of different tissues in a medical image. Researchers mostly focused on medical images of neurons, retina, lungs, digital pathology, breast, heart, abdomen and skeleton system to take advantage of the Deep Learning approach. This study reviews literature studies of recent years that utilized Deep Learning algorithms on medical images in order to present a general picture of the relevant literature.

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Bu Makaleye Atıf Yap

ASLAN, Z. (2019). ON THE USE OF DEEP LEARNING METHODS ON MEDICAL IMAGES. The International Journal of Energy and Engineering Sciences, Cilt: 3 - Sayı: 2, 1–15. https://ajindex.com/articles/805985

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Kaynakça (13)

  1. Haugeland, J. (1989). Artificial intelligence: The very idea. MIT press
  2. Fukushima, K., Miyake, S., & Ito, T. (1983). Neocognitron: A neural network model for a mechanism of visual pattern recognition. IEEE transactions on systems, man, and cybernetics, (5), 826-834
  3. Lo, S. C., Lou, S. L., Lin, J. S., Freedman, M. T., Chien, M. V., & Mun, S. K. (1995). Artificial convolution neural network techniques and applications for lung nodule detection. IEEE Transactions on Medical Imaging, 14(4), 711-718
  4. LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324
  5. Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems (pp. 1097-1105)
  6. Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S.,. & Berg, A. C. (2015). Imagenet large scale visual recognition challenge. International Journal of Computer Vision, 115(3), 211-252
  7. Bengio, Y., Courville, A., & Vincent, P. (2013). Representation learning: A review and new perspectives. IEEE transactions on pattern analysis and machine intelligence, 35(8), 1798-1828
  8. Ravi, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., & Yang, G. Z. (2017). Deep learning for health informatics. IEEE journal of biomedical and health informatics, 21(1), 4-21
  9. 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
  10. Shen, D., Wu, G., & Suk, H. I. (2017). Deep learning in medical image analysis. Annual review of biomedical engineering, 19, 221-248
  11. Nie, D., Zhang, H., Adeli, E., Liu, L., & Shen, D. (2016, October). 3D deep learning for multi-modal imaging-guided survival time prediction of brain tumor patients. In International Conference on Medical Image Computing and Computer-Assisted Intervention(pp. 212-220). Springer, Cham
  12. Xu, T., Zhang, H., Huang, X., Zhang, S., & Metaxas, D. N. (2016, October). Multimodal deep learning for cervical dysplasia diagnosis. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 115-123). Springer, Cham
  13. M. Anthimopoulos, S. Christodoulidis, L. Ebner, A. Christe, and S. Mougiakakou, “Lung pattern classification for interstitia