LinkedIn

 آمار

تعداد نشریات20
تعداد شماره‌ها1,113
تعداد مقالات10,208
تعداد مشاهده مقاله40,771,210
تعداد دریافت فایل اصل مقاله8,800,388
Tahmasebi Birgani, M J, Chegeni, N, Farhadi Birgani, F, Fatehi, D, Akbarizadeh, Gh, Shams, A. (1398). Optimization of Brain Tumor MR Image Classification Accuracy Using Optimal Threshold, PCA and Training ANFIS with Different Repetitions. سامانه مدیریت نشریات علمی, 9(2), 189-198. doi: 10.31661/jbpe.v0i0.790
M J Tahmasebi Birgani; N Chegeni; F Farhadi Birgani; D Fatehi; Gh Akbarizadeh; A Shams. "Optimization of Brain Tumor MR Image Classification Accuracy Using Optimal Threshold, PCA and Training ANFIS with Different Repetitions". سامانه مدیریت نشریات علمی, 9, 2, 1398, 189-198. doi: 10.31661/jbpe.v0i0.790
Tahmasebi Birgani, M J, Chegeni, N, Farhadi Birgani, F, Fatehi, D, Akbarizadeh, Gh, Shams, A. (1398). 'Optimization of Brain Tumor MR Image Classification Accuracy Using Optimal Threshold, PCA and Training ANFIS with Different Repetitions', سامانه مدیریت نشریات علمی, 9(2), pp. 189-198. doi: 10.31661/jbpe.v0i0.790
Tahmasebi Birgani, M J, Chegeni, N, Farhadi Birgani, F, Fatehi, D, Akbarizadeh, Gh, Shams, A. Optimization of Brain Tumor MR Image Classification Accuracy Using Optimal Threshold, PCA and Training ANFIS with Different Repetitions. سامانه مدیریت نشریات علمی, 1398; 9(2): 189-198. doi: 10.31661/jbpe.v0i0.790

Optimization of Brain Tumor MR Image Classification Accuracy Using Optimal Threshold, PCA and Training ANFIS with Different Repetitions

Journal of Biomedical Physics and Engineering
مقاله 7، دوره 9، شماره 2، خرداد و تیر 2019، صفحه 189-198    اصل مقاله (1.31 M)
نوع مقاله: Original Article
شناسه دیجیتال (DOI): 10.31661/jbpe.v0i0.790
نویسندگان
M J Tahmasebi Birgani1؛ N Chegeni2؛ F Farhadi Birgani* 2؛ D Fatehi3؛ Gh Akbarizadeh4؛ A Shams5
1Department of Radiation Oncology, Faculty of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
2Department of Medical Physics, Faculty of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
3Department of Medical Physics, Faculty of Medicine, Shahrekord University of Medical Sciences, Shahrekord, Iran
4Department of Electrical Engineering, Faculty of Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran
5Department of Radiation Oncology, Golestan Hospital, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
چکیده
Background: One of the leading causes of death is brain tumors. Accurate tumor classification leads to appropriate decision making and providing the most efficient treatment to the patients. This study aims to optimize brain tumor MR images classification accuracy using optimal threshold, PCA and training Adaptive Neuro Fuzzy Inference System (ANFIS) with different repetitions.
Material and Methods: The procedure used in this study consists of five steps: (1) T1, T2 weighted images collection, (2) tumor separation with different threshold levels, (3) feature extraction, (4) presence and absence of feature reduction applying principal component analysis (PCA) and (5) ANFIS classification with 0, 20 and 200 training repetitions.
Results: ANFIS accuracy was 40%, 80% and 97% for all features and 97%, 98.5% and 100% for the 6 selected features by PCA in 0, 20 and 200 training repetitions, respectively.
Conclusion: The findings of the present study demonstrated that accuracy can be raised up to 100% by using an optimized threshold method, PCA and increasing training repetitions.
کلیدواژه‌ها
MRI؛ Brain Tumor Detection؛ ANFIS؛ PCA؛ Training Repetition
مراجع
  1. Noureen E, Hassan K. Brain Tumor Detection Using Histogram Thresholding to Get the Threshold point. IOSR Journal of Electrical and Electronics Engineering. 2014;9:14-9.
  2. Zhang WL, Wang XZ, editors. Feature extraction and classification for human brain CT images. 19-22 Aug. 2007. Hong Kong: Machine Learning and Cybernetics, 2007 International Conference on; 2007: IEEE.
  3. Sahu G, Bhaiya P. A Survey Paper Based on the Classification of MRI Brain Images Using Soft Computing Techniques. International Journal of Emerging Technology and Advanced Engineering.2014;4:309-14.
  4. Borole VY, Nimbhore SS, Kawthekar DSS. Image Processing Techniques for Brain Tumor Detection: A Review. International Journal of Emerging Trends & Technology in Computer Science (IJETTCS). 2015;4:2.
  5. Roy S, Sadhu S, Bandyopadhyay SK, Bhattacharyya D, Kim T-H. Brain tumor classification using adaptive neuro-fuzzy inference system from MRI. International Journal of Bio-Science and Bio-Technology. 2016;8:203-18.
  6. Reddick WE, Glass JO, Cook EN, Elkin TD, Deaton RJ. Automated segmentation and classification of multispectral magnetic resonance images of brain using artificial neural networks. IEEE Trans Med Imaging. 1997;16:911-8. PubMed PMID: 9533591.
  7. Joshi DM, Rana N, Misra V, editors. Classification of brain cancer using artificial neural network. 7-10 May 2010. Kuala Lumpur: Electronic Computer Technology (ICECT), 2010 International Conference on; 2010: IEEE.
  8. Alfonse M, Salem A-BM. An automatic classification of brain tumors through MRI using support vector machine. Egyptian Computer Science Journal. 2016;40:11-21.
  9. Shubhangi D, Hiremath P, editors. Support vector machine (SVM) classifier for brain tumor detection. 23-24 January 2009. Mumbai: Proceedings of the International Conference on Advances in Computing, Communication and Control; 2009: ACM.
  10. Clark MC, Hall LO, Goldgof DB, Velthuizen R, Murtagh FR, Silbiger MS. Automatic tumor segmentation using knowledge-based techniques. IEEE Trans Med Imaging. 1998;17:187-201. doi: 10.1109/42.700731. PubMed PMID: 9688151.
  11. Rajini NH, Bhavani R, editors. Classification of MRI brain images using k-nearest neighbor and artificial neural network. 3-5 June 2011. Chennai: Recent Trends in Information Technology (ICRTIT), 2011 International Conference on; 2011: IEEE.
  12. Gering DT, Grimson WEL, Kikinis R, editors. Recognizing deviations from normalcy for brain tumor segmentation. Berlin: International Conference on Medical Image Computing and Computer-Assisted Intervention; 2002: Springer.
  13. Preetha R, Suresh G, editors. Performance analysis of fuzzy c means algorithm in automated detection of brain tumor. 27 Feb.-1 March 2014. Trichirappalli: Computing and Communication Technologies (WCCCT), 2014 World Congress on; 2014: IEEE.
  14. Singh A, editor Detection of brain tumor in MRI images, using combination of fuzzy c-means and SVM. 19-20 Feb. 2015. Noida: Signal Processing and Integrated Networks (SPIN), 2015 2nd International Conference on; 2015: IEEE.
  15. Hosseini MS, Zekri M. Review of Medical Image Classification using the Adaptive Neuro-Fuzzy Inference System. J Med Signals Sens. 2012;2:49-60. PubMed PMID: 23493054; PubMed Central PMCID: PMC3592505.
  16. Subasi A. Application of adaptive neuro-fuzzy inference system for epileptic seizure detection using wavelet feature extraction. Comput Biol Med. 2007;37:227-44. doi: 10.1016/j.compbiomed.2005.12.003. PubMed PMID: 16480706
  17. Rajasekaran MP, Sri Meena R. Application of adaptive neuro-fuzzy inference systems for MR image classification and tumour detection. Int J Biomed Eng Technol. 2012;9:133-46.
  18. Bhardwaj A, Siddhu KK. An approach to medical image classification using Neuro fuzzy logic and Anfis classifier. International Journal of Computer Trends and Technology. 2013;4:236-40.
  19. Zhang Y, Dong Z, Wu L, Wang S. A hybrid method for MRI brain image classification. Expert Syst Appl. 2011;38:10049-53.
  20. Rathi V, Palani S. Brain tumor MRI image classification with feature selection and extraction using linear discriminant analysis. arXiv preprint arXiv:12082128. 2012.
  21. Zhang N, Ruan S, Lebonvallet S, Liao Q, Zhu Y, editors. Multi-kernel SVM based classification for brain tumor segmentation of MRI multi-sequence. 7-10 Nov. 2009. Cairo: Image Processing (ICIP), 2009 16th IEEE International Conference on; 2009: IEEE.
  22. Jolliffe I. Principal component analysis. New Yourk: Wiley Online Library. 2005.
  23. Zhang Y, Wu L. An MR brain images classifier via principal component analysis and kernel support vector machine. Prog Electromagn Res. 2012;130:369-88.
  24. Lahmiri S, Boukadoum M, editors. Comparison of ANFIS and SVM for the classification of brain MRI Pathologies. 7-10 Aug. 2011. Seoul: Circuits and Systems (MWSCAS), 2011 IEEE 54th International Midwest Symposium on; 2011: IEEE.
  25. Sayah B, Tighiouart B. Brain tumour segmentation in MRI: knowledge-based system and region growing approach. Int J Biomed Eng Technol. 2014;14:71-89.
  26. Simonetti AW, Melssen WJ, Szabo de Edelenyi F, van Asten JJ, Heerschap A, Buydens LM. Combination of feature-reduced MR spectroscopic and MR imaging data for improved brain tumor classification. NMR Biomed. 2005;18:34-43. doi: 10.1002/nbm.919. PubMed PMID: 15657908.
  27. Zacharaki EI, Wang S, Chawla S, Soo Yoo D, Wolf R, Melhem ER, et al. Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme. Magn Reson Med. 2009;62:1609-18. doi: 10.1002/mrm.22147. PubMed PMID: 19859947; PubMed Central PMCID: PMC2863141.
  28. Jafari M, Shafaghi R. A hybrid approach for automatic tumor detection of brain MRI using support vector machine and genetic algorithm. Global Journal of Science, Engineering and Technology. 2012;3:1-8.
  29. Singh D, Kaur K. Classification of abnormalities in brain MRI images using GLCM, PCA and SVM. Int J Eng Adv Technol. 2012;1:2249-8958.
  30. Al-Badarneh A, Najadat H, Alraziqi AM, editors. A classifier to detect tumor disease in MRI brain images. 26-29 August 2012. Washington: Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012); 2012: IEEE Computer Society.
  31. Sharma M, Mukharjee S. Brain tumor segmentation using hybrid genetic algorithm and artificial neural network fuzzy inference system (anfis). International Journal of Fuzzy Logic Systems. 2012;2:31-42.
  32. Lakshmi A, Arivoli T. Computer aided diagnosis system for brain tumor detection and segmentation. J Theor Appl Inf Technol. 2014;6:561-74.
  33. El-Dahshan E, Salem A-BM, Younis TH. A hybrid technique for automatic MRI brain images classification. Studia Univ Babes-Bolyai, Informatica. 2009;54:55-67.
  34. Chaplot S, Patnaik L, Jagannathan N. Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network. Biomed Signal Process Control. 2006;1:86-92.
  35. Abdullah N, Chuen LW, Ngah UK, Ahmad KA, editors. Improvement of MRI brain classification using principal component analysis. 25-27 Nov. 2011. Penang: Control System, Computing and Engineering (ICCSCE), 2011 IEEE International Conference on; 2011: IEEE.
آمار
تعداد مشاهده مقاله: 742
تعداد دریافت فایل اصل مقاله: 249


سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب