Timpanik Membran Görüntü Özellikleri Kullanılarak Sınıflandırılması

Year 2021, Volume: 33 Issue: 2, 441 - 453, 15.09.2021

Erdal Başaran Zafer Cömert Yuksel Celık

https://doi.org/10.35234/fumbd.863118

Cited By: 1

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.

Keywords

Biyomedikal işaret işleme, yapay öğrenme, özellik çıkarımı, sınıflandırma

References

• M. Naghibolhosseini , G. R. Long, “Fractional-order modelling and simulation of human ear,” Int. J. Comput. Math., vol. 95, no. 6–7, pp. 1257–1273, Jul. 2018, doi: 10.1080/00207160.2017.1404038.
• S. S. Balu, A. B. Deoghare, and K. M. Pandey, “Design and Modeling of Human Middle Ear for Harmonic Response Analysis,” Jan. 2018, doi: 10.5281/ZENODO.1315782.
• W. Gao, W. Liang, and K. K. Tan, “Automated tube insertion on tympanic membrane based on vision-servo and tactile sensing,” IECON Proc. (Industrial Electron. Conf., no. c, pp. 2706–2711, 2014, doi: 10.1109/IECON.2014.7048889.
• A. P. J. Giese, S. Ali, A. Isaiah, I. Aziz, S. Riazuddin, and Z. M. Ahmed, “Genomics of Otitis Media (OM): Molecular Genetics Approaches to Characterize Disease Pathophysiology,” Front. Genet., vol. 11, p. 313, Apr. 2020, doi: 10.3389/fgene.2020.00313.
• K. Topal, “Olgularla Kulak Enfeksiyonları,” vol. 10. Selen Medya Yayıncılık Tanıtım ve Organizasyon Hizmetleri, pp. 44–47, 2018.
• A. G. M. Schilder et al., “Otitis media,” Nat. Rev. Dis. Prim., vol. 2, no. 1, p. 16063, 2016, doi: 10.1038/nrdp.2016.63.
• M. K. Park et al., “Differences in Antibiotic Resistance of MRSA Infections in Patients with Various Types of Otitis Media,” J. Int. Adv. Otol., vol. 14, no. 3, pp. 459–463, Dec. 2018, doi: 10.5152/iao.2018.5374.
• G. van Ingen et al., “Environmental determinants associated with acute otitis media in children: a longitudinal study,” Pediatr. Res., vol. 87, no. 1, pp. 163–168, 2020, doi: 10.1038/s41390-019-0540-3.
• O. R. A. R. Johanna M. Uitti, Miia K. Laine, Paula A. Tähtinen, “Symptoms and Otoscopic Signs in Bilateral and Unilateral Acute Otitis Media,” Off. J. Am. JAcademy Pediatr., vol. 131, no. e398, pp. 398–405, 2018, doi: 10.1542/peds.2012-1188.
• M. Maharjan, S. Phuyal, M. Shrestha, R. Bajracharya, “Chronic Otitis Media and Subsequent Hearing Loss in Children from the Himalayan Region Residing in Buddhist Monastic Schools of Nepal,” J. Otol., 2020, doi: https://doi.org/10.1016/j.joto.2020.09.001.
• 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, doi: 10.1109/EMBC.2014.6944662.
• L. Cheng, J. Liu, C. E. Roehm, and T. A. Valdez, “Enhanced video images for tympanic membrane characterization,” Proc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. EMBS, pp. 4002–4005, 2011, doi: 10.1109/IEMBS.2011.6090994.
• 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, doi: 10.1109/IDAP.2019.8875973.
• E. Başaran, Z. Cömert, A. Şengur, Ü. Budak, Y. Çelik, M. Toğaçar, “Normal ve Kronik Hastalıklı Orta Kulak İmgelerinin Evrişimsel Sinir Ağları Yöntemiyle Tespit Edilmesi,” Türkiye Bilişim Vakfı Bilgi. Bilim. ve Mühendisliği Derg., vol. 13, no. 1, pp. 1–10, Apr. 2020, Accessed: Apr. 26, 2020. [Online]. Available: http://dergipark.org.tr/tr/pub/tbbmd/issue/53711/657649.
• E. Başaran, Z. Cömert, Y. Çelik, “Convolutional neural network approach for automatic tympanic membrane detection and classification,” Biomed. Signal Process. Control, vol. 56, p. 101734, Feb. 2020, doi: 10.1016/J.BSPC.2019.101734.
• E. Basaran, Z. Comert, A. Sengur, U. Budak, Y. Celik, M. Togacar, “Chronic Tympanic Membrane Diagnosis based on Deep Convolutional Neural Network,” 2019, doi: 10.1109/UBMK.2019.8907070.
• Z. Cömert, “Otitis media için evrişimsel sinir ağlarına dayalı entegre bir tanı sistemi,” Bitlis Eren Üniversitesi Fen Bilim. Derg., vol. 8, no. 4, pp. 1498–1511, Dec. 2019, doi: 10.17798/bitlisfen.600636.
• C. Zafer, “Fusing fine-tuned deep features for recognizing different tympanic membranes,” Biocybern. Biomed. Eng., vol. 40, no. 1, pp. 40–51, 2020, doi: https://doi.org/10.1016/j.bbe.2019.11.001.
• M. Toğaçar, B. Ergen, 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, doi: 10.1016/J.BBE.2019.11.004.
• Y. Guo, Ü. Budak, A. Şengür, “A novel retinal vessel detection approach based on multiple deep convolution neural networks,” Comput. Methods Programs Biomed., vol. 167, pp. 43–48, Dec. 2018, doi: 10.1016/J.CMPB.2018.10.021.
• Ü. Budak, Z. Cömert, Z. N. Rashid, A. Şengür, M. Çıbuk, “Computer-aided diagnosis system combining FCN and Bi-LSTM model for efficient breast cancer detection from histopathological images,” Appl. Soft Comput., vol. 85, p. 105765, Dec. 2019, doi: 10.1016/J.ASOC.2019.105765.
• M. Toğaçar, B. Ergen, Z. Cömert, “COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches,” Comput. Biol. Med., vol. 121, p. 103805, 2020, doi: https://doi.org/10.1016/j.compbiomed.2020.103805.
• M. Nour, Z. Cömert, K. Polat, “A Novel Medical Diagnosis model for COVID-19 infection detection based on Deep Features and Bayesian Optimization,” Appl. Soft Comput., p. 106580, 2020, doi: https://doi.org/10.1016/j.asoc.2020.106580.
• 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.
• P. D. Kumar, “Feature Extraction and Selection of kidney Ultrasound Images Using GLCM and PCA,” Procedia Comput. Sci., vol. 167, pp. 1722–1731, 2020, doi: https://doi.org/10.1016/j.procs.2020.03.382.
• 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.
• A. Güner, Ö. F. Alçin, A. Şengür, “Automatic digital modulation classification using extreme learning machine with local binary pattern histogram features,” Measurement, vol. 145, pp. 214–225, Oct. 2019, doi: 10.1016/J.MEASUREMENT.2019.05.061.
• N. Dalal, B. Triggs, “Histograms of oriented gradients for human detection,” in 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), 2005, vol. 1, pp. 886–893 vol. 1, doi: 10.1109/CVPR.2005.177.
• M. F. Aslan, A. Durdu, K. Sabanci, and M. A. Mutluer, “CNN and HOG based comparison study for complete occlusion handling in human tracking,” Measurement, vol. 158, p. 107704, 2020, doi: https://doi.org/10.1016/j.measurement.2020.107704.
• 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.
• G. M. M. E Elahi, S. Kalra, L. Zinman, A. Genge, L. Korngut, Y.-H. Yang, “Texture classification of MR images of the brain in ALS using M-CoHOG: A multi-center study,” Comput. Med. Imaging Graph., vol. 79, p. 101659, 2020, doi: https://doi.org/10.1016/j.compmedimag.2019.101659.
• 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.