Timpanik Membran Görüntü Özellikleri Kullanılarak Sınıflandırılması
Year 2021,
, 441 - 453, 15.09.2021
Erdal Başaran
,
Zafer Cömert
,
Yuksel Celık
Abstract
Orta kulak inflamasyonu olarak bilinen otitis media rahatsızlığının teşhis edilmesi için otoskop cihazı ile zar bölgesine bakılarak karar verilmektedir. Dokusal özellik çıkarma algoritmaları, görüntüler üzerinde bölge tespiti ve görüntüye ait özelliklerin elde edilmesinde yaygın olarak kullanılmaktadır. Bu çalışmada gerekli yasal izinler alındıktan sonra elde edilen orta kulak görüntülerinde normal ve otitis media görüntülerinin ayırt edilmesi için literatürde yaygın olarak kullanılan gri seviyeli eş-oluşum matrisi, yerel ikili örüntüler, yönlü gradyanların histogram algoritmaları kullanılmıştır. Bu dokusal özellik çıkarma algoritmalarının görüntüleri sınıflandırma üzerinde başarıları incelendikten sonra her bir özellik setine görüntülere ait renk kanallarının ortalamaları da eklenerek bu özelliğin sınıflandırma başarısına etkisi incelenmiştir. Sonuç olarak tek başına bir dokusal özellik çıkarma algoritması kullanıldığında en iyi sonuçlar yerel ikili örüntü algoritması ile elde edilmiştir. Bu algoritmaya renk kanallarının ortalaması da eklendiği zaman sınıflandırma başarısını olumlu yönde etkilediği sonucuna varılmıştır. Sınıflandırma sonucunda % 78.67 doğruluk oranı elde edilmiştir.
References
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Year 2021,
, 441 - 453, 15.09.2021
Erdal Başaran
,
Zafer Cömert
,
Yuksel Celık
References
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- S. Salem Ghahfarrokhi H. Khodadadi, “Human brain tumor diagnosis using the combination of the complexity measures and texture features through magnetic resonance image,” Biomed. Signal Process. Control, vol. 61, p. 102025, 2020, doi: https://doi.org/10.1016/j.bspc.2020.102025.
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- A. Dongyao Jia, B. Zhengyi Li, and C. Chuanwang Zhang, “Detection of cervical cancer cells based on strong feature CNN-SVM network,” Neurocomputing, vol. 411, pp. 112–127, 2020, doi: https://doi.org/10.1016/j.neucom.2020.06.006.
- J. Tang, Q. Su, B. Su, S. Fong, W. Cao, and X. Gong, “Parallel ensemble learning of convolutional neural networks and local binary patterns for face recognition,” Comput. Methods Programs Biomed., vol. 197, p. 105622, 2020, doi: https://doi.org/10.1016/j.cmpb.2020.105622.
- F. Yuan, J. Shi, X. Xia, L. Zhang, S. Li, “Encoding pairwise Hamming distances of Local Binary Patterns for visual smoke recognition,” Comput. Vis. Image Underst., vol. 178, pp. 43–53, 2019, doi: https://doi.org/10.1016/j.cviu.2018.10.008.
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- X. Yan, Y. Zhang, D. Zhang, and N. Hou, “Multimodal image registration using histogram of oriented gradient distance and data-driven grey wolf optimizer,” Neurocomputing, Feb. 2020, doi: 10.1016/J.NEUCOM.2020.01.107.
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- Y. Hamed, A. Ibrahim Alzahrani, A. Shafie, Z. Mustaffa, M. Che Ismail, K. Kok Eng, “Two steps hybrid calibration algorithm of support vector regression and K-nearest neighbors,” Alexandria Eng. J., vol. 59, no. 3, pp. 1181–1190, 2020, doi: https://doi.org/10.1016/j.aej.2020.01.033.
- S. Zhang, “Cost-sensitive KNN classification,” Neurocomputing, vol. 391, pp. 234–242, 2020, doi: https://doi.org/10.1016/j.neucom.2018.11.101.
- Y. Chen, B. Chen, Y. Yao, C. Tan, J. Feng, “A spectroscopic method based on support vector machine and artificial neural network for fiber laser welding defects detection and classification,” NDT E Int., vol. 108, p. 102176, 2019, doi: https://doi.org/10.1016/j.ndteint.2019.102176.
- R. Arian, A. Hariri, A. Mehridehnavi, A. Fassihi, F. Ghasemi, “Protein Kinase Inhibitors’ Classification Using K-Nearest Neighbor Algorithm,” Comput. Biol. Chem., p. 107269, Apr. 2020, doi: 10.1016/J.COMPBIOLCHEM.2020.107269.
- M. Wadkar, F. Di Troia, and M. Stamp, “Detecting malware evolution using support vector machines,” Expert Syst. Appl., vol. 143, p. 113022, 2020, doi: https://doi.org/10.1016/j.eswa.2019.113022.
- J. Xu, W. Tan, and T. Li, “Predicting fan blade icing by using particle swarm optimization and support vector machine algorithm,” Comput. Electr. Eng., vol. 87, p. 106751, 2020, doi: https://doi.org/10.1016/j.compeleceng.2020.106751.
- J. Cervantes, F. Garcia-Lamont, L. Rodríguez-Mazahua, and A. Lopez, “A comprehensive survey on support vector machine classification: Applications, challenges and trends,” Neurocomputing, vol. 408, pp. 189–215, 2020, doi: https://doi.org/10.1016/j.neucom.2019.10.118.
- L. Tomak and Y. Bek, “İşlem Karakteristik Eğrisi Analizi ve Eğri Altında Kalan Alanların Karşılaştırılması,” Journal of Experimental and Clinical Medicine, vol. 27. Ondokuz Mayıs Üniversitesi, p., 2011, doi: 10.5835/jecm.v27i2.1569.
- L. Gao, L. Zhang, C. Liu, and S. Wu, “Handling imbalanced medical image data: A deep-learning-based one-class classification approach,” Artif. Intell. Med., vol. 108, p. 101935, 2020, doi: https://doi.org/10.1016/j.artmed.2020.101935.
- P. Shamsolmoali, M. Zareapoor, L. Shen, A. H. Sadka, and J. Yang, “Imbalanced data learning by minority class augmentation using capsule adversarial networks,” Neurocomputing, 2020, doi: https://doi.org/10.1016/j.neucom.2020.01.119.
- E. Duchesnay et al., “Feature selection and classification of imbalanced datasets: Application to PET images of children with autistic spectrum disorders,” Neuroimage, vol. 57, no. 3, pp. 1003–1014, 2011, doi: https://doi.org/10.1016/j.neuroimage.2011.05.011.