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Normal ve Kronik Hastalıklı Orta Kulak İmgelerinin Evrişimsel Sinir Ağları Yöntemiyle Tespit Edilmesi

Year 2020, Volume: 13 Issue: 1, 1 - 10, 13.04.2020

Erdal Başaran Zafer Cömert Abdulkadir Sengur Ümit Budak Yuksel Celık Mesut Toğaçar

Abstract

Orta kulak iltihabı kulak zarının arkasında sıvı birikmesi olarak bilinmektedir. Orta kulak iltihabının uzun süreli tedaviye yanıt vermemesi ve kulak zarının delinmesi ile karakterize olan kronik orta kulak iltihabı işitme kaybına bile sebep olabilen ciddi bir rahatsızlıktır. Bu çalışmada gerekli etik kurulu izni alındıktan sonra Özel Van Akdamar Hastanesinde gönüllü hastalardan otoskop cihazı ile elde edilen 598 adet normal orta kulak görüntüsü ve kronik hastalıklı orta kulak görüntüleri ile sınıflandırma işlemi gerçekleştirilmiştir. Son yıllarda yapay zekâ kapsamında değerlendirilen algoritmalar hemen her alanda kullanılmaktadır. Sağlık alanında da tanı ve karar destek sistemleri geliştirilerek başarılı çalışmalar yapılmaktadır. Bu çalışmada yapay zekâ algoritmalarından olan ve özellikle biyomedikal görüntü sınıflandırma çalışmalarında da iyi sonuçlar elde edilen evrişimsel sinir ağı mimarilerinden olan AlexNet, VGG16, VGG19, GoogleNet, ResNet18, ResNet50, ResNet101 modelleri kullanılmıştır. Deneysel çalışmalar sonucu VGG19 mimarisi ile %97.2067 başarı oranı elde edilmiştir. Evrişimsel sinir ağları yöntemi normal ve kronik orta kulak görüntülerini ayırt etmede başarılı bir yöntemdir.

Keywords

Kronik orta kulak iltihabı Evrişimsel sinir ağları Sinyal işleme

Thanks

UBMK19'da sunmus oldugumuz "Chronic Tympanic Membrane Diagnosis based on Deep Convolutional Neural Network" baslikli bildirimiz, TBV Bilgisayar Bilimleri ve Muhendisligi dergisinde secilip davet edildiğinden dolayı teşekkür ederiz, Saygılarımızla

References

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  • [2] R. Gün, “Kronik süpüratif otitis mediyada yas, hastalık süresi ve kolesteatom varlıgının sensorinöral isitme kaybı ile iliskisi,” Dicle Tıp Dergisi, , Sayı 2 , vol. Cilt: 36, no. sayı 2, pp. 117–122, 2009.
  • [3] E. Yorgancılar et al., “Complications of chronic suppurative otitis media: a retrospective review,” Eur. Arch. Oto-Rhino-Laryngology, vol. 270, no. 1, pp. 69–76, 2013.
  • [4] O. Pakır, “Kronik süpüratif otitis mediada medikal Tedavinin cerrahi tedavinin zamanlamasındaki Rolü,” Zonguldak Üniversitesi, 2015.
  • [5] Z. Cömert and A. F. Kocamaz, “Fetal Hypoxia Detection Based on Deep Convolutional Neural Network with Transfer Learning Approach,” in Software Engineering and Algorithms in Intelligent Systems, 2019, pp. 239–248.
  • [6] E. Deniz, A. Sengür, Z. Kadiroğuglu, Y. Guo, V. Bajaj, and Ü. Budak, “Transfer learning based histopathologic image classification for breast cancer detection,” Heal. Inf. Sci. Syst., vol. 6, no. 1, p. 18, 2018.
  • [7] Z. Cömert, A. Şengür, Y. Akbulut, Ü. Budak, A. F. Kocamaz, and V. Bajaj, “Efficient approach for digitization of the cardiotocography signals,” Phys. A Stat. Mech. its Appl., vol. 537, p. 122725, Jan. 2020.
  • [8] Ü. Budak, Y. Guo, E. Tanyildizi, and A. Şengür, “Cascaded deep convolutional encoder-decoder neural networks for efficient liver tumor segmentation,” Med. Hypotheses, vol. 134, p. 109431, Jan. 2020.
  • [9] U. Budak, V. Bajaj, Y. Akbulut, O. Atila, and A. Sengur, “An Effective Hybrid Model for EEG-Based Drowsiness Detection,” IEEE Sens. J., vol. 19, no. 17, pp. 7624–7631, 2019.
  • [10] D. D. Heck Junior, V., Wangenheim, A.v., Abdala, “Computational Techniques for Accompaniment and Measuring of Otology Pathologies,” pp. 0–5, 2007.
  • [11] C. K. Shie, H. T. Chang, F. C. Fan, C. J. Chen, T. Y. Fang, and P. C. Wang, “A hybrid feature-based segmentation and classification system for the computer aided self-diagnosis of otitis media,” 2014 36th Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. EMBC 2014, pp. 4655–4658, 2014.
  • [12] H. C. Myburgh, W. H. Van Zijl, D. Swanepoel, S. Hellström, and C. Laurent, “EBioMedicine Otitis Media Diagnosis for Developing Countries Using Tympanic Membrane Image-Analysis,” vol. 5, pp. 156–160, 2016.
  • [13] E. Başaran, A. Şengür, Z. Cömert, Ü. Budak, Y. Çelık, and S. Velappan, “Normal and Acute Tympanic Membrane Diagnosis based on Gray Level Co-Occurrence Matrix and Artificial Neural Networks,” in 2019 International Artificial Intelligence and Data Processing Symposium (IDAP), 2019, pp. 1–6.
  • [14] E. Başaran, Z. Cömert, and Y. Çelik, “Convolutional neural network approach for automatic tympanic membrane detection and classification,” Biomed. Signal Process. Control, vol. 56, p. 101734, Feb. 2020.
  • [15] C. Zafer, “Fusing fine-tuned deep features for recognizing different tympanic membranes,” Biocybern. Biomed. Eng., Nov. 2019.
  • [16] V. Bajaj, M. Pawar, V. K. Meena, M. Kumar, A. Sengur, and Y. Guo, “Computer-aided diagnosis of breast cancer using bi-dimensional empirical mode decomposition,” Neural Comput. Appl., 2017.
  • [17] Z. Zhao, Y. Zhang, Z. Comert, and Y. Deng, “Computer-Aided Diagnosis System of Fetal Hypoxia Incorporating Recurrence Plot With Convolutional Neural Network,” Front. Physiol., vol. 10, p. 255, 2019.
  • [18] M. Toğaçar, B. Ergen, and Z. Cömert, “Detection of lung cancer on chest CT images using minimum redundancy maximum relevance feature selection method with convolutional neural networks,” Biocybern. Biomed. Eng., Nov. 2019.
  • [19] M. Toğaçar and B. Ergen, “Deep Learning Approach for Classification of Breast Canser,” in 2018 International Conference on Artificial Intelligence and Data Processing (IDAP), 2018, pp. 1–5.
  • [20] A. Feng-Ping and L. Zhi-Wen, “Medical image segmentation algorithm based on feedback mechanism convolutional neural network,” Biomed. Signal Process. Control, vol. 53, p. 101589, Aug. 2019.
  • [21] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” in Proceedings of the 25th International Conference on Neural Information Processing Systems - Volume 1, 2012, pp. 1097–1105.
  • [22] A. Arı, “Derin Öğrenme Tabanlı Beyin Mr Görüntülerinden Beyin Tümörlerinin Tespit Edilmesi Ve Sınıflandırılması,” İnönü University, 2019.
  • [23] F. İ. Eyiokur, D. Yaman, and H. K. Ekenel, “Sketch classification with deep learning models,” in 2018 26th Signal Processing and Communications Applications Conference (SIU), 2018, pp. 1–4.
  • [24] C. Szegedy et al., “Going deeper with convolutions,” in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 1–9.
  • [25] Y. Altuntaş, A. F. Kocamaz, Z. Cömert, R. Cengiz, and M. Esmeray, “Identification of Haploid Maize Seeds using Gray Level Co-occurrence Matrix and Machine Learning Techniques,” in 2018 International Conference on Artificial Intelligence and Data Processing (IDAP), 2018, pp. 1–5.
  • [26] A. Gómez-Ríos, S. Tabik, J. Luengo, A. Shihavuddin, B. Krawczyk, and F. Herrera, “Towards highly accurate coral texture images classification using deep convolutional neural networks and data augmentation,” Expert Syst. Appl., vol. 118, pp. 315–328, Mar. 2019.

Year 2020, Volume: 13 Issue: 1, 1 - 10, 13.04.2020

Erdal Başaran Zafer Cömert Abdulkadir Sengur Ümit Budak Yuksel Celık Mesut Toğaçar

Abstract

References

  • [1] T. Tham, L. Rahman, and P. Costantino, “Efficacy of a non-invasive middle ear aeration device in children with recurrent otitis media: A randomized controlled trial protocol,” Contemp. Clin. Trials Commun., vol. 12, pp. 92–97, Dec. 2018.
  • [2] R. Gün, “Kronik süpüratif otitis mediyada yas, hastalık süresi ve kolesteatom varlıgının sensorinöral isitme kaybı ile iliskisi,” Dicle Tıp Dergisi, , Sayı 2 , vol. Cilt: 36, no. sayı 2, pp. 117–122, 2009.
  • [3] E. Yorgancılar et al., “Complications of chronic suppurative otitis media: a retrospective review,” Eur. Arch. Oto-Rhino-Laryngology, vol. 270, no. 1, pp. 69–76, 2013.
  • [4] O. Pakır, “Kronik süpüratif otitis mediada medikal Tedavinin cerrahi tedavinin zamanlamasındaki Rolü,” Zonguldak Üniversitesi, 2015.
  • [5] Z. Cömert and A. F. Kocamaz, “Fetal Hypoxia Detection Based on Deep Convolutional Neural Network with Transfer Learning Approach,” in Software Engineering and Algorithms in Intelligent Systems, 2019, pp. 239–248.
  • [6] E. Deniz, A. Sengür, Z. Kadiroğuglu, Y. Guo, V. Bajaj, and Ü. Budak, “Transfer learning based histopathologic image classification for breast cancer detection,” Heal. Inf. Sci. Syst., vol. 6, no. 1, p. 18, 2018.
  • [7] Z. Cömert, A. Şengür, Y. Akbulut, Ü. Budak, A. F. Kocamaz, and V. Bajaj, “Efficient approach for digitization of the cardiotocography signals,” Phys. A Stat. Mech. its Appl., vol. 537, p. 122725, Jan. 2020.
  • [8] Ü. Budak, Y. Guo, E. Tanyildizi, and A. Şengür, “Cascaded deep convolutional encoder-decoder neural networks for efficient liver tumor segmentation,” Med. Hypotheses, vol. 134, p. 109431, Jan. 2020.
  • [9] U. Budak, V. Bajaj, Y. Akbulut, O. Atila, and A. Sengur, “An Effective Hybrid Model for EEG-Based Drowsiness Detection,” IEEE Sens. J., vol. 19, no. 17, pp. 7624–7631, 2019.
  • [10] D. D. Heck Junior, V., Wangenheim, A.v., Abdala, “Computational Techniques for Accompaniment and Measuring of Otology Pathologies,” pp. 0–5, 2007.
  • [11] C. K. Shie, H. T. Chang, F. C. Fan, C. J. Chen, T. Y. Fang, and P. C. Wang, “A hybrid feature-based segmentation and classification system for the computer aided self-diagnosis of otitis media,” 2014 36th Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. EMBC 2014, pp. 4655–4658, 2014.
  • [12] H. C. Myburgh, W. H. Van Zijl, D. Swanepoel, S. Hellström, and C. Laurent, “EBioMedicine Otitis Media Diagnosis for Developing Countries Using Tympanic Membrane Image-Analysis,” vol. 5, pp. 156–160, 2016.
  • [13] E. Başaran, A. Şengür, Z. Cömert, Ü. Budak, Y. Çelık, and S. Velappan, “Normal and Acute Tympanic Membrane Diagnosis based on Gray Level Co-Occurrence Matrix and Artificial Neural Networks,” in 2019 International Artificial Intelligence and Data Processing Symposium (IDAP), 2019, pp. 1–6.
  • [14] E. Başaran, Z. Cömert, and Y. Çelik, “Convolutional neural network approach for automatic tympanic membrane detection and classification,” Biomed. Signal Process. Control, vol. 56, p. 101734, Feb. 2020.
  • [15] C. Zafer, “Fusing fine-tuned deep features for recognizing different tympanic membranes,” Biocybern. Biomed. Eng., Nov. 2019.
  • [16] V. Bajaj, M. Pawar, V. K. Meena, M. Kumar, A. Sengur, and Y. Guo, “Computer-aided diagnosis of breast cancer using bi-dimensional empirical mode decomposition,” Neural Comput. Appl., 2017.
  • [17] Z. Zhao, Y. Zhang, Z. Comert, and Y. Deng, “Computer-Aided Diagnosis System of Fetal Hypoxia Incorporating Recurrence Plot With Convolutional Neural Network,” Front. Physiol., vol. 10, p. 255, 2019.
  • [18] M. Toğaçar, B. Ergen, and Z. Cömert, “Detection of lung cancer on chest CT images using minimum redundancy maximum relevance feature selection method with convolutional neural networks,” Biocybern. Biomed. Eng., Nov. 2019.
  • [19] M. Toğaçar and B. Ergen, “Deep Learning Approach for Classification of Breast Canser,” in 2018 International Conference on Artificial Intelligence and Data Processing (IDAP), 2018, pp. 1–5.
  • [20] A. Feng-Ping and L. Zhi-Wen, “Medical image segmentation algorithm based on feedback mechanism convolutional neural network,” Biomed. Signal Process. Control, vol. 53, p. 101589, Aug. 2019.
  • [21] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” in Proceedings of the 25th International Conference on Neural Information Processing Systems - Volume 1, 2012, pp. 1097–1105.
  • [22] A. Arı, “Derin Öğrenme Tabanlı Beyin Mr Görüntülerinden Beyin Tümörlerinin Tespit Edilmesi Ve Sınıflandırılması,” İnönü University, 2019.
  • [23] F. İ. Eyiokur, D. Yaman, and H. K. Ekenel, “Sketch classification with deep learning models,” in 2018 26th Signal Processing and Communications Applications Conference (SIU), 2018, pp. 1–4.
  • [24] C. Szegedy et al., “Going deeper with convolutions,” in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 1–9.
  • [25] Y. Altuntaş, A. F. Kocamaz, Z. Cömert, R. Cengiz, and M. Esmeray, “Identification of Haploid Maize Seeds using Gray Level Co-occurrence Matrix and Machine Learning Techniques,” in 2018 International Conference on Artificial Intelligence and Data Processing (IDAP), 2018, pp. 1–5.
  • [26] A. Gómez-Ríos, S. Tabik, J. Luengo, A. Shihavuddin, B. Krawczyk, and F. Herrera, “Towards highly accurate coral texture images classification using deep convolutional neural networks and data augmentation,” Expert Syst. Appl., vol. 118, pp. 315–328, Mar. 2019.
There are 26 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Makaleler(Araştırma)
Authors

Erdal Başaran 0000-0001-8569-2998

Zafer Cömert 0000-0001-5256-7648

Abdulkadir Sengur 0000-0003-1614-2639

Ümit Budak 0000-0003-4082-383X

Yuksel Celık 0000-0002-7117-9736

Mesut Toğaçar 0000-0002-8264-3899

Publication Date April 13, 2020
Published in Issue Year 2020 Volume: 13 Issue: 1

Cite

APA Başaran, E., Cömert, Z., Sengur, A., Budak, Ü., et al. (2020). Normal ve Kronik Hastalıklı Orta Kulak İmgelerinin Evrişimsel Sinir Ağları Yöntemiyle Tespit Edilmesi. Türkiye Bilişim Vakfı Bilgisayar Bilimleri Ve Mühendisliği Dergisi, 13(1), 1-10.
AMA Başaran E, Cömert Z, Sengur A, Budak Ü, Celık Y, Toğaçar M. Normal ve Kronik Hastalıklı Orta Kulak İmgelerinin Evrişimsel Sinir Ağları Yöntemiyle Tespit Edilmesi. TBV-BBMD. April 2020;13(1):1-10.
Chicago Başaran, Erdal, Zafer Cömert, Abdulkadir Sengur, Ümit Budak, Yuksel Celık, and Mesut Toğaçar. “Normal Ve Kronik Hastalıklı Orta Kulak İmgelerinin Evrişimsel Sinir Ağları Yöntemiyle Tespit Edilmesi”. Türkiye Bilişim Vakfı Bilgisayar Bilimleri Ve Mühendisliği Dergisi 13, no. 1 (April 2020): 1-10.
EndNote Başaran E, Cömert Z, Sengur A, Budak Ü, Celık Y, Toğaçar M (April 1, 2020) Normal ve Kronik Hastalıklı Orta Kulak İmgelerinin Evrişimsel Sinir Ağları Yöntemiyle Tespit Edilmesi. Türkiye Bilişim Vakfı Bilgisayar Bilimleri ve Mühendisliği Dergisi 13 1 1–10.
IEEE E. Başaran, Z. Cömert, A. Sengur, Ü. Budak, Y. Celık, and M. Toğaçar, “Normal ve Kronik Hastalıklı Orta Kulak İmgelerinin Evrişimsel Sinir Ağları Yöntemiyle Tespit Edilmesi”, TBV-BBMD, vol. 13, no. 1, pp. 1–10, 2020.
ISNAD Başaran, Erdal et al. “Normal Ve Kronik Hastalıklı Orta Kulak İmgelerinin Evrişimsel Sinir Ağları Yöntemiyle Tespit Edilmesi”. Türkiye Bilişim Vakfı Bilgisayar Bilimleri ve Mühendisliği Dergisi 13/1 (April 2020), 1-10.
JAMA Başaran E, Cömert Z, Sengur A, Budak Ü, Celık Y, Toğaçar M. Normal ve Kronik Hastalıklı Orta Kulak İmgelerinin Evrişimsel Sinir Ağları Yöntemiyle Tespit Edilmesi. TBV-BBMD. 2020;13:1–10.
MLA Başaran, Erdal et al. “Normal Ve Kronik Hastalıklı Orta Kulak İmgelerinin Evrişimsel Sinir Ağları Yöntemiyle Tespit Edilmesi”. Türkiye Bilişim Vakfı Bilgisayar Bilimleri Ve Mühendisliği Dergisi, vol. 13, no. 1, 2020, pp. 1-10.
Vancouver Başaran E, Cömert Z, Sengur A, Budak Ü, Celık Y, Toğaçar M. Normal ve Kronik Hastalıklı Orta Kulak İmgelerinin Evrişimsel Sinir Ağları Yöntemiyle Tespit Edilmesi. TBV-BBMD. 2020;13(1):1-10.
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