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Yıl 2019, Cilt: 2 Sayı: 1, 28 - 40, 30.04.2019

Pakize Erdoğmuş

https://doi.org/10.35377/saucis.02.01.541366
Cited By: 1

Öz

Kaynakça

  • Felder, Richard M., and Linda K. Silverman. "Learning and teaching styles in engineering education." Engineering education78.7 (1988): 674-681.
  • LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. "Deep learning." nature 521.7553 (2015): 436.
  • Litjens, Geert, et al. "A survey on deep learning in medical image analysis." Medical image analysis 42 (2017): 60-88.
  • https://www.webofknowledge.com, Last acces date: 14.03.2019.
  • Gulshan, Varun, et al. "Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs." Jama 316.22 (2016): 2402-2410.
  • Abràmoff, Michael David, et al. "Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning." Investigative ophthalmology & visual science 57.13 (2016): 5200-5206.
  • Gargeya, Rishab, and Theodore Leng. "Automated identification of diabetic retinopathy using deep learning." Ophthalmology124.7 (2017): 962-969.
  • Quellec, Gwenolé, et al. "Deep image mining for diabetic retinopathy screening." Medical image analysis 39 (2017): 178-193.
  • Schlegl, Thomas, et al. "Fully automated detection and quantification of macular fluid in OCT using deep learning." Ophthalmology 125.4 (2018): 549-558.
  • Van Grinsven, Mark JJP, et al. "Fast convolutional neural network training using selective data sampling: application to hemorrhage detection in color fundus images." IEEE transactions on medical imaging 35.5 (2016): 1273-1284.
  • Abbas, Qaisar, et al. "Automatic recognition of severity level for diagnosis of diabetic retinopathy using deep visual features." Medical & biological engineering & computing 55.11 (2017): 1959-1974.
  • Dutta, Suvajit, et al. "Classification of diabetic retinopathy images by using deep learning models." International Journal of Grid and Distributed Computing 11.1 (2018): 89-106.
  • Zhang, Defeng, et al. "Automatic localization and segmentation of optical disk based on faster R-CNN and level set in fundus image." Medical Imaging 2018: Image Processing. Vol. 10574. International Society for Optics and Photonics, 2018.
  • Vijay Kotu, Bala Deshpande, Chapter 10 - Deep Learning, Editor(s): Vijay Kotu, Bala Deshpande, Data Science (Second Edition), Morgan Kaufmann, 2019, Pages 307-342, ISBN 9780128147610.
  • Hebb, D. 0. (1949) The Organization of Behavior (Wiley, New York).
  • Eccles, J. G. (1953) The Neurophysiological Basis of Mind (Clarendon, Oxford).
  • Hopfield, John J. "Neural networks and physical systems with emergent collective computational abilities." Proceedings of the national academy of sciences 79.8 (1982): 2554-2558.
  • Sünderhauf, Niko, et al. "On the performance of convnet features for place recognition." arXiv preprint arXiv:1501.04158 (2015). https://www.image.net/, Last acces date: 14.03.2019.
  • Huang, Gao, et al. "Densely connected convolutional networks." Proceedings of the IEEE conference on computer vision and pattern recognition. 2017.
  • Hinton, Geoffrey E., Simon Osindero, and Yee-Whye Teh. "A fast learning algorithm for deep belief nets." Neural computation 18.7 (2006): 1527-1554.
  • Huang, Jin, and Charles X. Ling. "Using AUC and accuracy in evaluating learning algorithms." IEEE Transactions on knowledge and Data Engineering17.3 (2005): 299-310
  • Gulshan, Varun, et al. "Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs." Jama316.22 (2016): 2402-2410.
  • Kamnitsas, Konstantinos, et al. "Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation." Medical image analysis 36 (2017): 61-78.
  • Anthimopoulos, Marios, et al. "Lung pattern classification for interstitial lung diseases using a deep convolutional neural network." IEEE transactions on medical imaging 35.5 (2016): 1207-1216.
  • Prasoon, Adhish, et al. "Deep feature learning for knee cartilage segmentation using a triplanar convolutional neural network." International conference on medical image computing and computer-assisted intervention. Springer, Berlin, Heidelberg, 2013.
  • S. Pereira, A. Pinto, V. Alves and C. A. Silva, "Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images," in IEEE Transactions on Medical Imaging, vol. 35, no. 5, pp. 1240-1251, May 2016.
  • Havaei, Mohammad, et al. "Brain tumor segmentation with deep neural networks." Medical image analysis 35 (2017): 18-31.
  • Milletari, Fausto, Nassir Navab, and Seyed-Ahmad Ahmadi. "V-net: Fully convolutional neural networks for volumetric medical image segmentation." 2016 Fourth International Conference on 3D Vision (3DV). IEEE, 2016.
  • Bar, Yaniv, et al. "Chest pathology detection using deep learning with non-medical training." 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI). IEEE, 2015.
  • Al Rahhal, Mohamad Mahmoud, et al. "Deep learning approach for active classification of electrocardiogram signals." Information Sciences 345 (2016): 340-354.
  • Alipanahi, Babak, et al. "Predicting the sequence specificities of DNA-and RNA-binding proteins by deep learning." Nature biotechnology 33.8 (2015): 831.
  • Roth, Holger R., et al. "Deeporgan: Multi-level deep convolutional networks for automated pancreas segmentation." International conference on medical image computing and computer-assisted intervention. Springer, Cham, 2015.
  • Chen, Hao, et al. "Standard plane localization in fetal ultrasound via domain transferred deep neural networks." IEEE journal of biomedical and health informatics 19.5 (2015): 1627-1636.
  • Huynh, Benjamin Q., Hui Li, and Maryellen L. Giger. "Digital mammographic tumor classification using transfer learning from deep convolutional neural networks." Journal of Medical Imaging3.3 (2016): 034501.
  • Gargeya, Rishab, and Theodore Leng. "Automated identification of diabetic retinopathy using deep learning." Ophthalmology124.7 (2017): 962-969.
  • Fu, Huazhu, et al. "Retinal vessel segmentation via deep learning network and fully-connected conditional random fields." 2016 IEEE 13th international symposium on biomedical imaging (ISBI). IEEE, 2016.
  • Poplin, Ryan, et al. "Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning." Nature Biomedical Engineering 2.3 (2018): 158.
  • Carneiro, Gustavo, Jacinto C. Nascimento, and António Freitas. "The segmentation of the left ventricle of the heart from ultrasound data using deep learning architectures and derivative-based search methods." IEEE Transactions on Image Processing21.3 (2012): 968-982.
  • Yildirim, Ozal, Ru San Tan, and U. Rajendra Acharya. "An efficient compression of ECG signals using deep convolutional autoencoders." Cognitive Systems Research 52 (2018): 198-211.
  • Shin, Hoo-Chang, et al. "Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning." IEEE transactions on medical imaging 35.5 (2016): 1285-1298.
  • Litjens, Geert, et al. "A survey on deep learning in medical image analysis." Medical image analysis 42 (2017): 60-88.
  • Tajbakhsh, Nima, et al. "Convolutional neural networks for medical image analysis: Full training or fine tuning?." IEEE transactions on medical imaging 35.5 (2016): 1299-1312.
  • Greenspan, Hayit, Bram Van Ginneken, and Ronald M. Summers. "Guest editorial deep learning in medical imaging: Overview and future promise of an exciting new technique." IEEE Transactions on Medical Imaging 35.5 (2016): 1153-1159.
  • Shen, Dinggang, Guorong Wu, and Heung-Il Suk. "Deep learning in medical image analysis." Annual review of biomedical engineering 19 (2017): 221-248.
  • Işın, Ali, Cem Direkoğlu, and Melike Şah. "Review of MRI-based brain tumor image segmentation using deep learning methods." Procedia Computer Science 102 (2016): 317-324.

Deep Learning Performance on Medical Image, Data and Signals

Yıl 2019, Cilt: 2 Sayı: 1, 28 - 40, 30.04.2019

Pakize Erdoğmuş

https://doi.org/10.35377/saucis.02.01.541366
Cited By: 1

Öz

Bu
çalışmada, 2009-2019 yılları arasında Tıpta derin öğrenme ile ilgili yapılmış
çalışmalar, derin öğrenmenin Tıbbı görüntü, veri ve sinyaller üzerine
başarısını gözlemlemek için araştırılmıştır. Web of Science’tan elde edilen
çalışmalar değerlendirilmiş ve atıf sayısına göre seçilmişlerdir. Çalışmalar
yayın yılı, derin ağ yapısı, kullanılan veritabanı ve değerlendirme kriterine
göre tablo haline getirilmiştir. The results have shown that the deep learning
network structures, applied on fundus images, have attained nearly %99 percent accuracy.
Sonuçlar retinal fundus görüntüleri uygulanan derin öğrenme ağ yapılarının
doğruluklarının %99’lara ulaştığını göstemektedir.  Bu aralıktaki çalışmaların çoğu radyoloji ve
nükleer tıp alanında yapılmış olsa de sonuçlar henüz %80-90 aralığında
görülmektedir. Bu sonuçlar bilgisayar destekli teşhis sistemlerinin çok yakın
bir gelecekte tam performans ile kullanılacağını göstermektedir.

Anahtar Kelimeler

edical data deep learning convolutional neural networks

Kaynakça

  • Felder, Richard M., and Linda K. Silverman. "Learning and teaching styles in engineering education." Engineering education78.7 (1988): 674-681.
  • LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. "Deep learning." nature 521.7553 (2015): 436.
  • Litjens, Geert, et al. "A survey on deep learning in medical image analysis." Medical image analysis 42 (2017): 60-88.
  • https://www.webofknowledge.com, Last acces date: 14.03.2019.
  • Gulshan, Varun, et al. "Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs." Jama 316.22 (2016): 2402-2410.
  • Abràmoff, Michael David, et al. "Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning." Investigative ophthalmology & visual science 57.13 (2016): 5200-5206.
  • Gargeya, Rishab, and Theodore Leng. "Automated identification of diabetic retinopathy using deep learning." Ophthalmology124.7 (2017): 962-969.
  • Quellec, Gwenolé, et al. "Deep image mining for diabetic retinopathy screening." Medical image analysis 39 (2017): 178-193.
  • Schlegl, Thomas, et al. "Fully automated detection and quantification of macular fluid in OCT using deep learning." Ophthalmology 125.4 (2018): 549-558.
  • Van Grinsven, Mark JJP, et al. "Fast convolutional neural network training using selective data sampling: application to hemorrhage detection in color fundus images." IEEE transactions on medical imaging 35.5 (2016): 1273-1284.
  • Abbas, Qaisar, et al. "Automatic recognition of severity level for diagnosis of diabetic retinopathy using deep visual features." Medical & biological engineering & computing 55.11 (2017): 1959-1974.
  • Dutta, Suvajit, et al. "Classification of diabetic retinopathy images by using deep learning models." International Journal of Grid and Distributed Computing 11.1 (2018): 89-106.
  • Zhang, Defeng, et al. "Automatic localization and segmentation of optical disk based on faster R-CNN and level set in fundus image." Medical Imaging 2018: Image Processing. Vol. 10574. International Society for Optics and Photonics, 2018.
  • Vijay Kotu, Bala Deshpande, Chapter 10 - Deep Learning, Editor(s): Vijay Kotu, Bala Deshpande, Data Science (Second Edition), Morgan Kaufmann, 2019, Pages 307-342, ISBN 9780128147610.
  • Hebb, D. 0. (1949) The Organization of Behavior (Wiley, New York).
  • Eccles, J. G. (1953) The Neurophysiological Basis of Mind (Clarendon, Oxford).
  • Hopfield, John J. "Neural networks and physical systems with emergent collective computational abilities." Proceedings of the national academy of sciences 79.8 (1982): 2554-2558.
  • Sünderhauf, Niko, et al. "On the performance of convnet features for place recognition." arXiv preprint arXiv:1501.04158 (2015). https://www.image.net/, Last acces date: 14.03.2019.
  • Huang, Gao, et al. "Densely connected convolutional networks." Proceedings of the IEEE conference on computer vision and pattern recognition. 2017.
  • Hinton, Geoffrey E., Simon Osindero, and Yee-Whye Teh. "A fast learning algorithm for deep belief nets." Neural computation 18.7 (2006): 1527-1554.
  • Huang, Jin, and Charles X. Ling. "Using AUC and accuracy in evaluating learning algorithms." IEEE Transactions on knowledge and Data Engineering17.3 (2005): 299-310
  • Gulshan, Varun, et al. "Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs." Jama316.22 (2016): 2402-2410.
  • Kamnitsas, Konstantinos, et al. "Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation." Medical image analysis 36 (2017): 61-78.
  • Anthimopoulos, Marios, et al. "Lung pattern classification for interstitial lung diseases using a deep convolutional neural network." IEEE transactions on medical imaging 35.5 (2016): 1207-1216.
  • Prasoon, Adhish, et al. "Deep feature learning for knee cartilage segmentation using a triplanar convolutional neural network." International conference on medical image computing and computer-assisted intervention. Springer, Berlin, Heidelberg, 2013.
  • S. Pereira, A. Pinto, V. Alves and C. A. Silva, "Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images," in IEEE Transactions on Medical Imaging, vol. 35, no. 5, pp. 1240-1251, May 2016.
  • Havaei, Mohammad, et al. "Brain tumor segmentation with deep neural networks." Medical image analysis 35 (2017): 18-31.
  • Milletari, Fausto, Nassir Navab, and Seyed-Ahmad Ahmadi. "V-net: Fully convolutional neural networks for volumetric medical image segmentation." 2016 Fourth International Conference on 3D Vision (3DV). IEEE, 2016.
  • Bar, Yaniv, et al. "Chest pathology detection using deep learning with non-medical training." 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI). IEEE, 2015.
  • Al Rahhal, Mohamad Mahmoud, et al. "Deep learning approach for active classification of electrocardiogram signals." Information Sciences 345 (2016): 340-354.
  • Alipanahi, Babak, et al. "Predicting the sequence specificities of DNA-and RNA-binding proteins by deep learning." Nature biotechnology 33.8 (2015): 831.
  • Roth, Holger R., et al. "Deeporgan: Multi-level deep convolutional networks for automated pancreas segmentation." International conference on medical image computing and computer-assisted intervention. Springer, Cham, 2015.
  • Chen, Hao, et al. "Standard plane localization in fetal ultrasound via domain transferred deep neural networks." IEEE journal of biomedical and health informatics 19.5 (2015): 1627-1636.
  • Huynh, Benjamin Q., Hui Li, and Maryellen L. Giger. "Digital mammographic tumor classification using transfer learning from deep convolutional neural networks." Journal of Medical Imaging3.3 (2016): 034501.
  • Gargeya, Rishab, and Theodore Leng. "Automated identification of diabetic retinopathy using deep learning." Ophthalmology124.7 (2017): 962-969.
  • Fu, Huazhu, et al. "Retinal vessel segmentation via deep learning network and fully-connected conditional random fields." 2016 IEEE 13th international symposium on biomedical imaging (ISBI). IEEE, 2016.
  • Poplin, Ryan, et al. "Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning." Nature Biomedical Engineering 2.3 (2018): 158.
  • Carneiro, Gustavo, Jacinto C. Nascimento, and António Freitas. "The segmentation of the left ventricle of the heart from ultrasound data using deep learning architectures and derivative-based search methods." IEEE Transactions on Image Processing21.3 (2012): 968-982.
  • Yildirim, Ozal, Ru San Tan, and U. Rajendra Acharya. "An efficient compression of ECG signals using deep convolutional autoencoders." Cognitive Systems Research 52 (2018): 198-211.
  • Shin, Hoo-Chang, et al. "Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning." IEEE transactions on medical imaging 35.5 (2016): 1285-1298.
  • Litjens, Geert, et al. "A survey on deep learning in medical image analysis." Medical image analysis 42 (2017): 60-88.
  • Tajbakhsh, Nima, et al. "Convolutional neural networks for medical image analysis: Full training or fine tuning?." IEEE transactions on medical imaging 35.5 (2016): 1299-1312.
  • Greenspan, Hayit, Bram Van Ginneken, and Ronald M. Summers. "Guest editorial deep learning in medical imaging: Overview and future promise of an exciting new technique." IEEE Transactions on Medical Imaging 35.5 (2016): 1153-1159.
  • Shen, Dinggang, Guorong Wu, and Heung-Il Suk. "Deep learning in medical image analysis." Annual review of biomedical engineering 19 (2017): 221-248.
  • Işın, Ali, Cem Direkoğlu, and Melike Şah. "Review of MRI-based brain tumor image segmentation using deep learning methods." Procedia Computer Science 102 (2016): 317-324.
Toplam 45 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Bölüm Makaleler
Yazarlar

Pakize Erdoğmuş 0000-0003-2172-5767

Yayımlanma Tarihi 30 Nisan 2019
Gönderilme Tarihi 18 Mart 2019
Kabul Tarihi 24 Nisan 2019
Yayımlandığı Sayı Yıl 2019Cilt: 2 Sayı: 1

Kaynak Göster

IEEE P. Erdoğmuş, “Deep Learning Performance on Medical Image, Data and Signals”, SAUCIS, c. 2, sy. 1, ss. 28–40, 2019, doi: 10.35377/saucis.02.01.541366.

Cited By

Derin öğrenme temelli nesne tespiti algoritmaları kullanılarak kişiye özgü reklam sunulması
Balıkesir Üniversitesi Fen Bilimleri Enstitüsü Dergisi
https://doi.org/10.25092/baunfbed.878224
 Kapak Resmi İndir

29070  The papers in this journal are licensed under a Creative Commons Attribution-NonCommercial 4.0 International License