Research Article
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Year 2021, Volume: 36 Issue: 2, 997 - 1012, 05.03.2021
https://doi.org/10.17341/gazimmfd.762056

Abstract

References

  • Tiwari, A., Srivastava, S., Pant, M., Brain tumor segmentation and classification from magnetic resonance images: Review of selected methods from 2014 to 2019. Pattern Recognition Letters, 131, 244-260, 2020.
  • Gordillo, N., Montseny, E., Sobrevillac, P., State of the art survey on MRI brain tumor segmentation. Magnetic Resonance Imaging, 31(8), 1426-1438, 2013.
  • Smistad, E., Falch, T.L, Bozorgi, M., Elster, A.C., Lindseth, F., Medical image segmentation on GPUs – A comprehensive review, Medical Image Analysis. 20(1), 1-18, 2015.
  • Hinton, G.E., S. Osindero, Y.-W. Teh, 2006. A fast learning algorithm for deep belief nets. Neural Computation, 18 (7), 1527-1554, 2006.
  • Bengio, Y., LeCun, Y., Scaling learning algorithms towards AI. MIT Press, 2007.
  • Krizhevsky, A., Sutskever, I., G. Hinton G., ImageNet classification with deep convolutional neural networks. NIPS'12 Proceedings of the 25th International Conference on Neural Information Processing Systems, 1, 1097-1105, 2012.
  • Mazurowski, M.A., Buda, M., Saha, A., Bashir, M.R., Deep learning in radiology: an overview of the concepts and a survey of the state of the art. arXiv:1802.08717, 2018.
  • Vieira, S., Pinaya, W.H.L., Mechelli, A., Using deep learning to investigate the neuroimaging correlates of psychiatric and neurological disorders: Methods and applications. Neuroscience and Biobehavioral Reviews, 74, 58-75, 2017.
  • Liu, S.Q., Liu, S.D., Cai, W.D., Pujol, S., Kikinis, R., Feng, D.G., Early diagnosis of Alzheimer’s disease with deep learning. in Proc. 2014 IEEE 11th Int. Symposium on Biomedical Imaging (ISBI), Beijing, China, 1015-1018, 2014.
  • Zaharchuk, G., Gong, E., Wintermark, M., Rubin, D., Langlotz, C.P., Deep Learning in Neuroradiology, AJNR Am. J. Neuroradiol., 39(10), 1776-1784, 2018.
  • Liu, J., Pan, Y., Li, M., Chen, Z., Tang, L., Lu, C., Wang, J., Applications of Deep Learning to MRI Images: A Survey, Big Data Mining and Analytics, 1 (1), 1-18, 2018.
  • Zhang, W.L., Li, R.J., Deng, H.T., Wang, L., Lin, W.L., Ji, S.W., Shen, D.G., Deep convolutional Neural networks for multi-modality isointense infant brain image segmentation, NeuroImage, 108, 214-224, 2015.
  • Pereira, S., Pinto, A., Alves, V., Silva, C.A., Brain tumor segmentation using convolutional neural networks in MRI images, IEEE Trans. Med. Imaging, 35(5), 1240-1251, 2016.
  • Suk, H.I., Lee, S.W, Shen, D.G., Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis, NeuroImage, 101, 569-582, 2014.
  • Vieira, S., Pinaya, W.H.L., Mechelli, A., Using deep learning to investigate the neuroimaging correlates of psychiatric and neurological disorders: Methods and applications, Neuroscience and Biobehavioral Reviews, 74, 58-75, 2017.
  • Amin, J., Sharif, M., Yasmin, M., Fernandes, S.L., A distinctive approach in brain tumor detection and classification using MRI, Pattern Recognit. Lett., 1-10, 2017.
  • Kebir, S.T., Mekaoui, S., An efficient methodology of brain abnormalities detection using CNN deep learning network, in: international Conference on Applied Smart Systems (ICASS), Medea, Algeria, 1-5, 2018.
  • Talo, M., Baloglu, U.B., Yıldırım, Ö., Acharya, U.R., Application of deep transfer learning for automated brain abnormality classification using MR images, Cognit. Syst. Res., 54, 176-188, 2019.
  • Figshare brain tumor dataset. https://doi.org/10.6084/m9.figshare.1512427.v5. Erişim tarihi Haziran 30, 2020.
  • Cheng, J., Huang, W., Cao, S., Yang, R., Yang, W., Yun Z, et al., Enhanced Performance of Brain Tumor Classification via Tumor Region Augmentation and Partition. PLoS ONE, 10(10), e0140381. doi:10.1371/journal.pone.0140381, 2015.
  • Deepak, S., Ameer, P.M., Brain tumor classification using deep CNN features via transfer learning, Comput. Biol. Med., 111, 103345, 1-7, 2019.
  • Cheng, J., Huang, W., Cao, S., Yang, R., Yang, W., Yun, Z., Feng, Q., Enhanced performance of brain tumor classification via tumor region augmentation and partition, PLoS One, 10(10), e0140381, 2015.
  • Ismael, M.R., Abdel-Qader, I., Brain tumor classification via statistical features and back-propagation neural network, IEEE International Conference on Electro/Information Technology, EIT, 0252-0257, 2018.
  • Pashaei, A., Sajedi, H., Jazayeri, N., Brain tumor classification via convolutional neural network and extreme learning machines, IEEE 8th International Conference on Computer and Knowledge Engineering, ICCKE, 314-319, 2018.
  • Abiwinanda, N., Hanif, M., Hesaputra, S.T., Handayani, A., Mengko, T.R., Brain tumor classification using convolutional neural network, Springer World Congress on Medical Physics and Biomedical Engineering, 183-189, 2018.
  • Afshar, P., Plataniotis, K.N., Mohammadi, A., Capsule networks for brain tumor classification based on MRI images and course tumor boundaries, IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP, 1368-1372, 2019.
  • Badža, M.M., Barjaktarović, M.C., Classification of Brain Tumors from MRI Images Using a Convolutional Neural Network, Applied Sciences, 10(6), 1999, 1-13, 2020.
  • Bhanothu, Y., Kamalakannan, A., Rajamanickam, G., Detection and Classification of Brain Tumor in MRI Images using Deep Convolutional Network, 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS), 00, 248-252, 2020.
  • Jakub, N., Michal, M., Kawulok Michal, Data Augmentation for Brain-Tumor Segmentation: A Review, Frontiers in Computational Neuroscience, 13(83), 1-18. 2019.
  • Çelik,G., Fatih Talu, M.F., Çekişmeli üretken ağ modellerinin görüntü üretme performanslarının incelenmesi, BAUN Fen Bil. Enst. Dergisi, 22(1), 181-192, 2020.
  • Yia, X., Walia, E., Babyn, P., Generative adversarial network in medical imaging: A review, Medical Image Analysis, 58, 101552, 2019.
  • Frid-Adar, M., Diamant, I., Klang, E., Amitai, M., Goldberger, J., Greenspan, H., GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification, arXiv:1803.01229, 2018.
  • Iqbal, T., Ali, H., Generative Adversarial Network for Medical Images (MI-GAN), Journal of Medical Systems 42, 231, 2018.
  • Kazuhiro, K., Werner, R.A., Toriumi, F., Javadi, M.S., Pomper, M.G., Solnes, L.B., Verde, F., Higuchi, T., Ro, S.P., Generative Adversarial Networks for the Creation of Realistic Artificial Brain Magnetic Resonance Images, 4(4), 159-163, 2018.
  • Armanious, K., Jiang, C., Fischer, M., Küstner, T., Hepp, T., Nikolaou, K., Gatidis, S., Yang, B., MedGAN: Medical image translation using GANs, Computerized Medical Imaging and Graphics, 79, 101684, 1-13, 2020.
  • Huang, G., Liu, Z., Maaten, L. van der, Weinberger, K.Q., Densely Connected Convolutional Networks, arXiv:1608.06993, 2017.
  • Huang, G., Liu, Z., Pleiss, G., Maaten, L. van der, Weinberger, K.Q. Convolutional Networks with Dense Connectivity, IEEE Transactions on Pattern Analysis and Machine Intelligence, 1-12, 2019.
  • Karakis, R., Tez, M., Kılıç, Y.A., Kuru, B., Guler, I., A genetic algorithm model based on artificial neural network for prediction of the axillary lymph node status in breast cancer, Engineering Applications of Artificial Intelligence, 26 (3), 945-950, 2013.

Veri çoğaltma kullanılarak derin öğrenme ile beyin tümörlerinin sınıflandırılması

Year 2021, Volume: 36 Issue: 2, 997 - 1012, 05.03.2021
https://doi.org/10.17341/gazimmfd.762056

Abstract

Tıbbi görüntü sınıflandırma, veriyi istenilen sayıda sınıfa ayrıştırma işlemidir. Son yıllarda, Manyetik Rezonans Görüntüleme (MRG) beyin tümörlerinin tespit edilmesinde ve tanısında yaygın olarak kullanılmaktadır. Bu çalışmada, üç farklı beyin tümörünün(gliyom, menenjiyom ve hipofiz bezesi) T1 ağırlıklı MR görüntüleri üzerinde evrişimsel sinir ağı (ESA) kullanılarak sınıflandırılması ve aksiyel, koronel ve sagital MR kesitlerinin sınıflandırmadaki etkinliğinin belirlenmesi amaçlanmıştır. Ağırlıklar, başlangıçta ImageNet veri kümesi için eğitilmiş DenseNet121 ağından ESA’ya transfer edilmiştir. Ayrıca, afin dönüşümü ve piksel-seviye dönüşümü MR görüntülerinde veri çoğaltmada kullanılmıştır. Eğitilen ESA’nın tam bağlantılı ilk katmanından elde edilen öznitelikler, destek vektör makinesi(DVM), k en yakın komşu (kNN) ve Bayes yöntemleriyle de sınıflandırılmıştır. Bu sınıflandırıcıların başarısı test veriseti üzerinde duyarlılık, belirlilik, doğruluk, eğri altında kalan alan ve korelasyon katsayısı ile ölçülmüştür. ESA, ve ESA tabanlı DVM, kNN ve Bayes sınıflandırıcılarının elde ettiği doğruluk değerleri sırasıyla 0.9860, 0.9979, 0.9907 ve 0.8933’ dür. Beyin tümör sınıflandırma için önerilen ESA tabanlı DVM modeli literatürdeki benzer çalışmalardan daha yüksek performans değerleri elde etmiştir. Ayrıca beyin tümör tipini görüntülerden belirlemede beyin koronel kesitleri diğer kesitlere göre daha etkindir.

References

  • Tiwari, A., Srivastava, S., Pant, M., Brain tumor segmentation and classification from magnetic resonance images: Review of selected methods from 2014 to 2019. Pattern Recognition Letters, 131, 244-260, 2020.
  • Gordillo, N., Montseny, E., Sobrevillac, P., State of the art survey on MRI brain tumor segmentation. Magnetic Resonance Imaging, 31(8), 1426-1438, 2013.
  • Smistad, E., Falch, T.L, Bozorgi, M., Elster, A.C., Lindseth, F., Medical image segmentation on GPUs – A comprehensive review, Medical Image Analysis. 20(1), 1-18, 2015.
  • Hinton, G.E., S. Osindero, Y.-W. Teh, 2006. A fast learning algorithm for deep belief nets. Neural Computation, 18 (7), 1527-1554, 2006.
  • Bengio, Y., LeCun, Y., Scaling learning algorithms towards AI. MIT Press, 2007.
  • Krizhevsky, A., Sutskever, I., G. Hinton G., ImageNet classification with deep convolutional neural networks. NIPS'12 Proceedings of the 25th International Conference on Neural Information Processing Systems, 1, 1097-1105, 2012.
  • Mazurowski, M.A., Buda, M., Saha, A., Bashir, M.R., Deep learning in radiology: an overview of the concepts and a survey of the state of the art. arXiv:1802.08717, 2018.
  • Vieira, S., Pinaya, W.H.L., Mechelli, A., Using deep learning to investigate the neuroimaging correlates of psychiatric and neurological disorders: Methods and applications. Neuroscience and Biobehavioral Reviews, 74, 58-75, 2017.
  • Liu, S.Q., Liu, S.D., Cai, W.D., Pujol, S., Kikinis, R., Feng, D.G., Early diagnosis of Alzheimer’s disease with deep learning. in Proc. 2014 IEEE 11th Int. Symposium on Biomedical Imaging (ISBI), Beijing, China, 1015-1018, 2014.
  • Zaharchuk, G., Gong, E., Wintermark, M., Rubin, D., Langlotz, C.P., Deep Learning in Neuroradiology, AJNR Am. J. Neuroradiol., 39(10), 1776-1784, 2018.
  • Liu, J., Pan, Y., Li, M., Chen, Z., Tang, L., Lu, C., Wang, J., Applications of Deep Learning to MRI Images: A Survey, Big Data Mining and Analytics, 1 (1), 1-18, 2018.
  • Zhang, W.L., Li, R.J., Deng, H.T., Wang, L., Lin, W.L., Ji, S.W., Shen, D.G., Deep convolutional Neural networks for multi-modality isointense infant brain image segmentation, NeuroImage, 108, 214-224, 2015.
  • Pereira, S., Pinto, A., Alves, V., Silva, C.A., Brain tumor segmentation using convolutional neural networks in MRI images, IEEE Trans. Med. Imaging, 35(5), 1240-1251, 2016.
  • Suk, H.I., Lee, S.W, Shen, D.G., Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis, NeuroImage, 101, 569-582, 2014.
  • Vieira, S., Pinaya, W.H.L., Mechelli, A., Using deep learning to investigate the neuroimaging correlates of psychiatric and neurological disorders: Methods and applications, Neuroscience and Biobehavioral Reviews, 74, 58-75, 2017.
  • Amin, J., Sharif, M., Yasmin, M., Fernandes, S.L., A distinctive approach in brain tumor detection and classification using MRI, Pattern Recognit. Lett., 1-10, 2017.
  • Kebir, S.T., Mekaoui, S., An efficient methodology of brain abnormalities detection using CNN deep learning network, in: international Conference on Applied Smart Systems (ICASS), Medea, Algeria, 1-5, 2018.
  • Talo, M., Baloglu, U.B., Yıldırım, Ö., Acharya, U.R., Application of deep transfer learning for automated brain abnormality classification using MR images, Cognit. Syst. Res., 54, 176-188, 2019.
  • Figshare brain tumor dataset. https://doi.org/10.6084/m9.figshare.1512427.v5. Erişim tarihi Haziran 30, 2020.
  • Cheng, J., Huang, W., Cao, S., Yang, R., Yang, W., Yun Z, et al., Enhanced Performance of Brain Tumor Classification via Tumor Region Augmentation and Partition. PLoS ONE, 10(10), e0140381. doi:10.1371/journal.pone.0140381, 2015.
  • Deepak, S., Ameer, P.M., Brain tumor classification using deep CNN features via transfer learning, Comput. Biol. Med., 111, 103345, 1-7, 2019.
  • Cheng, J., Huang, W., Cao, S., Yang, R., Yang, W., Yun, Z., Feng, Q., Enhanced performance of brain tumor classification via tumor region augmentation and partition, PLoS One, 10(10), e0140381, 2015.
  • Ismael, M.R., Abdel-Qader, I., Brain tumor classification via statistical features and back-propagation neural network, IEEE International Conference on Electro/Information Technology, EIT, 0252-0257, 2018.
  • Pashaei, A., Sajedi, H., Jazayeri, N., Brain tumor classification via convolutional neural network and extreme learning machines, IEEE 8th International Conference on Computer and Knowledge Engineering, ICCKE, 314-319, 2018.
  • Abiwinanda, N., Hanif, M., Hesaputra, S.T., Handayani, A., Mengko, T.R., Brain tumor classification using convolutional neural network, Springer World Congress on Medical Physics and Biomedical Engineering, 183-189, 2018.
  • Afshar, P., Plataniotis, K.N., Mohammadi, A., Capsule networks for brain tumor classification based on MRI images and course tumor boundaries, IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP, 1368-1372, 2019.
  • Badža, M.M., Barjaktarović, M.C., Classification of Brain Tumors from MRI Images Using a Convolutional Neural Network, Applied Sciences, 10(6), 1999, 1-13, 2020.
  • Bhanothu, Y., Kamalakannan, A., Rajamanickam, G., Detection and Classification of Brain Tumor in MRI Images using Deep Convolutional Network, 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS), 00, 248-252, 2020.
  • Jakub, N., Michal, M., Kawulok Michal, Data Augmentation for Brain-Tumor Segmentation: A Review, Frontiers in Computational Neuroscience, 13(83), 1-18. 2019.
  • Çelik,G., Fatih Talu, M.F., Çekişmeli üretken ağ modellerinin görüntü üretme performanslarının incelenmesi, BAUN Fen Bil. Enst. Dergisi, 22(1), 181-192, 2020.
  • Yia, X., Walia, E., Babyn, P., Generative adversarial network in medical imaging: A review, Medical Image Analysis, 58, 101552, 2019.
  • Frid-Adar, M., Diamant, I., Klang, E., Amitai, M., Goldberger, J., Greenspan, H., GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification, arXiv:1803.01229, 2018.
  • Iqbal, T., Ali, H., Generative Adversarial Network for Medical Images (MI-GAN), Journal of Medical Systems 42, 231, 2018.
  • Kazuhiro, K., Werner, R.A., Toriumi, F., Javadi, M.S., Pomper, M.G., Solnes, L.B., Verde, F., Higuchi, T., Ro, S.P., Generative Adversarial Networks for the Creation of Realistic Artificial Brain Magnetic Resonance Images, 4(4), 159-163, 2018.
  • Armanious, K., Jiang, C., Fischer, M., Küstner, T., Hepp, T., Nikolaou, K., Gatidis, S., Yang, B., MedGAN: Medical image translation using GANs, Computerized Medical Imaging and Graphics, 79, 101684, 1-13, 2020.
  • Huang, G., Liu, Z., Maaten, L. van der, Weinberger, K.Q., Densely Connected Convolutional Networks, arXiv:1608.06993, 2017.
  • Huang, G., Liu, Z., Pleiss, G., Maaten, L. van der, Weinberger, K.Q. Convolutional Networks with Dense Connectivity, IEEE Transactions on Pattern Analysis and Machine Intelligence, 1-12, 2019.
  • Karakis, R., Tez, M., Kılıç, Y.A., Kuru, B., Guler, I., A genetic algorithm model based on artificial neural network for prediction of the axillary lymph node status in breast cancer, Engineering Applications of Artificial Intelligence, 26 (3), 945-950, 2013.
There are 38 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Makaleler
Authors

Kali Gurkahraman 0000-0002-0697-125X

Rukiye Karakış 0000-0002-1797-3461

Publication Date March 5, 2021
Submission Date July 4, 2020
Acceptance Date December 7, 2020
Published in Issue Year 2021 Volume: 36 Issue: 2

Cite

APA Gurkahraman, K., & Karakış, R. (2021). Veri çoğaltma kullanılarak derin öğrenme ile beyin tümörlerinin sınıflandırılması. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 36(2), 997-1012. https://doi.org/10.17341/gazimmfd.762056
AMA Gurkahraman K, Karakış R. Veri çoğaltma kullanılarak derin öğrenme ile beyin tümörlerinin sınıflandırılması. GUMMFD. March 2021;36(2):997-1012. doi:10.17341/gazimmfd.762056
Chicago Gurkahraman, Kali, and Rukiye Karakış. “Veri çoğaltma kullanılarak Derin öğrenme Ile Beyin tümörlerinin sınıflandırılması”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 36, no. 2 (March 2021): 997-1012. https://doi.org/10.17341/gazimmfd.762056.
EndNote Gurkahraman K, Karakış R (March 1, 2021) Veri çoğaltma kullanılarak derin öğrenme ile beyin tümörlerinin sınıflandırılması. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 36 2 997–1012.
IEEE K. Gurkahraman and R. Karakış, “Veri çoğaltma kullanılarak derin öğrenme ile beyin tümörlerinin sınıflandırılması”, GUMMFD, vol. 36, no. 2, pp. 997–1012, 2021, doi: 10.17341/gazimmfd.762056.
ISNAD Gurkahraman, Kali - Karakış, Rukiye. “Veri çoğaltma kullanılarak Derin öğrenme Ile Beyin tümörlerinin sınıflandırılması”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 36/2 (March 2021), 997-1012. https://doi.org/10.17341/gazimmfd.762056.
JAMA Gurkahraman K, Karakış R. Veri çoğaltma kullanılarak derin öğrenme ile beyin tümörlerinin sınıflandırılması. GUMMFD. 2021;36:997–1012.
MLA Gurkahraman, Kali and Rukiye Karakış. “Veri çoğaltma kullanılarak Derin öğrenme Ile Beyin tümörlerinin sınıflandırılması”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, vol. 36, no. 2, 2021, pp. 997-1012, doi:10.17341/gazimmfd.762056.
Vancouver Gurkahraman K, Karakış R. Veri çoğaltma kullanılarak derin öğrenme ile beyin tümörlerinin sınıflandırılması. GUMMFD. 2021;36(2):997-1012.