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IDSS-based Two stage classification of brain tumor using SVM

  • Sanjeeva PolepakaEmail author
  • Ch. Srinivasa Rao
  • M. Chandra Mohan
Original Paper
  • 15 Downloads
Part of the following topical collections:
  1. Internet Of Medical Things In E-Health

Abstract

The computer and image processing has a significant role in detecting tumor area. The decision support systems for human brain MR images are essentially encouraged with the requirement of attaining maximal achievable efficiency and the motivation of the approach which is to enhance the performance of Computer-Aided Diagnosis (CAD) system to detect a tumor in the human brain. Even though numerous support systems have been introduced in the past, this is still an open problem seeking for an accurate and robust decision support system. The Interactive Diagnosis Support System (IDSS) approach has addressed the limitations of nonillumination and low contrast of a brain tumor MR image that influences the procedure of accurate image classification. Thus, the IDSS is implemented in three phases namely image preprocessing for enhancing non-illuminated features, feature extraction and image classification which is accomplished using two-stage interactive SVM Classification. The local binary patterns are detected in the feature extraction for accurate classification of usual and unusual brain MR Images. The experimental outcomes for this approach are carried out using MATLAB R2016a and evaluated using the brain images downloaded from the Internet. The performance metrics such as structured similarity index, sensitivity, specificity and accuracy were used to assess the IDSS-based tumor classification system. When compared with the traditional classifiers such as ANFIS, Backpropagation and K-NN, the IDSS approach has significant brain tumor classification accuracy.

Keywords

IDSS Brain tumor Tumor segmentation Tumor classification LBP SVM Non-illumination Feature extraction CAD 

Notes

Funding

This is self funding by the primary Author, Sanjeeva Polepaka.

Compliance with ethical standards

Conflict of interest

No conflict of Interest with any person, Company or institution.

Informed consent

Not applicable.

References

  1. 1.
    Patel J, Doshi K. A study of segmentation methods for detection of tumor in brain MRI. Adv Electron Electr Eng. 2014;4(3):279–84.Google Scholar
  2. 2.
    Shaikhli SDS, Yang MY, Rosenhahn B. Brain tumor classification using sparse coding and dictionary learning, IEEE Conf. on Image Processing. 2014; 2774–2778.Google Scholar
  3. 3.
    Mustaqeem A, Javed A, Fatima T. An efficient brain tumor detection algorithm using watershed and thresholding-based segmentation. International Journal of Image Graphics and Signal Processing. 2012;4(10):34–9.Google Scholar
  4. 4.
    Shakeel PM, Baskar S, Dhulipala VRS, et al. Cloud based framework for diagnosis of diabetes mellitus using K-means clustering. Health Inf Sci Syst. 2018;6:16.  https://doi.org/10.1007/s13755-018-0054-0.Google Scholar
  5. 5.
    Fujita H, Uchiyama Y, Nakagawa T, Fukuoka D, Hatanaka Y, Hara T. Computer-aided diagnosis: the emerging of three CAD systems induced by Japanese health care needs. Comput Methods Prog Biomed. 2008;92(3):238–48.Google Scholar
  6. 6.
    Sridhar KP, Baskar S, Shakeel PM, et al. Developing brain abnormality recognize system using multi-objective pattern producing neural network. J Ambient Intell Human Comput. 2018.  https://doi.org/10.1007/s12652-018-1058-y.
  7. 7.
    Prajapati SJ, Jadhav KR. Brain tumor detection by various image segmentation techniques with introduction to non-negative matrix factorization. Brain. 2015;4(3):600–3.Google Scholar
  8. 8.
    Dipak Kumar K, Amiya H. Automatic brain tumor detection and isolation of tumor cells from MRI images. International journal of computer applications. 2012;39(1):26–30.Google Scholar
  9. 9.
    Arimura H, Magome T, Yamashita Y, Yamamoto D. Computer-aided diagnosis systems for brain diseases in magnetic resonance images. Algorithms. 2009;2(3):925–52.MathSciNetzbMATHGoogle Scholar
  10. 10.
    Cherkassky V, Mulier F. Learning from data: Concepts, theory and methods, (2nd Ed.). John Wiley and Sons, 2007.Google Scholar
  11. 11.
    Mohsen H, Dahshan E, Salem A. A machine learning technique for MRI brain images, Proc. 8thIEEE Conf. on Informatics and Systems. 2012.Google Scholar
  12. 12.
    Yamamoto D, Arimura H, Kakeda S, Magome T, Yamashita Y, Toyofuku F. Computer-aided detection of multiple sclerosis lesions in brain magnetic resonance images: false positive reduction scheme consisted of rule-based, level set method, and support vector machine. Comput Med Imaging Graph. 2010;34(5):404–13.Google Scholar
  13. 13.
    Ojala T, Pietikainen M, Maenpaa T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns, IEEETransactions on pattern analysis and machine. Intelligence. 2002;24(7):971–87.Google Scholar
  14. 14.
    Li Z, Liu G, Yang Y, You J. Scaleand rotation-invariant local binary pattern using scale-adaptive texton and subuniform-based circular shift. IEEE Transactions Image Processing. 2012;21(4):2130–40.MathSciNetzbMATHGoogle Scholar
  15. 15.
    Suruliandi A, Meena K, Rose RR. Local binary pattern and its derivatives for face recognition. IET Comput Vis. 2012;6(5):480–8.Google Scholar
  16. 16.
    Nawarathna R, Oh J, Muthukudage J, Tavanapong W, Wong J, Groen P, et al. Abnormal image detection in endoscopy videos using a filter bank and localbinary patterns. Neurocomputing. 2014;144:70–91.Google Scholar
  17. 17.
    Ryusuke N, Kazuhiro F. HEp-2 cell classification using rotation invariant co-occurrence among local binary patterns. Pattern Recogn. 2014;47(7):2428–36.Google Scholar
  18. 18.
    Jia X, Yang X, Cao K, Zang Y, Dai NZR, Zhu X, et al. Multi-scale local binary pattern with filters for spoof fingerprint detection. Inf Sci. 2014;268:91–102.Google Scholar
  19. 19.
    Gu J, Liu C. Feature local binary patterns with application to eye detection. Neurocomputing. 2013;113:138–52.Google Scholar
  20. 20.
    Nguyen DT, Ogunbona PO, Li W. A novel shape-based non-redundant local binary pattern descriptor for object detection. Pattern Recogn. 2013;46(5):1485–500.Google Scholar
  21. 21.
    Guo Z, Zhang L, Zhang D. Rotation invariant texture classification using LBPvariance (LBPV) with global matching. Pattern Recogn. 2010;43(3):706–19.zbMATHGoogle Scholar
  22. 22.
    Liao S, Law MK, Chung AS. Dominant local binary patterns for texture classification. IEEE Trans Image Process. 2009;18(5):1107–18.MathSciNetzbMATHGoogle Scholar
  23. 23.
    Ojala T, Pietikainen M. Harwood D. a comparative study of texture measures with classification based on feature distributions. Pattern Recogn. 1996;29(1):51–9.Google Scholar
  24. 24.
    Ojala T, Valkealahti K, Oja E, Pietikainen M. Texture discriminant with multidimensional distributions of signed gray-level differences. Pattern Recogn. 2001;34(3):727–39.zbMATHGoogle Scholar
  25. 25.
    Subrahmanym M, Maheswari RP, Balasubramanian R. Local maximum edge binary patterns: a new descriptor for image retrieval and object tracking. Signal Process. 2012;92(6):1467–79.Google Scholar
  26. 26.
    Zhu C, Wang R. Local multiple patterns based multiresolution gray-scale and rotation invariant texture classification. Inf Sci. 2012;187:93–108.Google Scholar
  27. 27.
    Sachdeva J, Kumar V, Gupta I, Khandelwal N, Ahuja CK. Segmentation, feature extraction, and multiclass brain tumor classification. Journal of Digit Imaging. 2013;26:1141–50.Google Scholar
  28. 28.
    Zacharaki EI, Wang S, Chawla S, Yoo DS, Wolf R, Mehem ER, et al. Classification of brain tumor and grade using MRI texture in a machine learning technique. Magn Reson Med. 2009;62:1609–18.Google Scholar
  29. 29.
    Georgiardis P, Cavouras D, Kalatzis I, Kagadis GC, Malamas M, Nikifordis G, et al. Nonlinearleast square feature transformations for improving the performance of probabilistic neural networks in classifying human brain tumors on MRI. Lect Notes Comput Sci. 2007;4707:239–47.Google Scholar
  30. 30.
    Mohamed Shakeel P, Baskar S, Sarma Dhulipala VR, Mishra S. Mustafa Musa Jaber. Maintaining security and privacy in health care system using learning based deep-Q-networks. J Med Syst. 2018;42:186.Google Scholar
  31. 31.
    Pourhashemi A, Haghighatnia S, Moghaddam RK. Identification of tumor-immune system via recurrent neural network. Health Technol. 2014;4(1):27–30.Google Scholar
  32. 32.
    Deva Kumar S, Gnaneswara RN. Wavelet-based Diabetic Retinopathy Image Enhancement in Blood Vessels, 46th Conf. on Computers and Industrial Engineering. 2016.Google Scholar
  33. 33.
    Gnaneswara RN, Ramakrishna SV, Deva Kumar S, Venkata Rao M. An improved IHBM using smoothing projections. Intl Journal of Control Theory and Applications. 2015;8(1):339–48.Google Scholar
  34. 34.
    Ojala T, Valkealahti K, Oja E, Pietikäinen M. Texture discrimination with multidimensional distributions of signed gray-level differences. Pattern Recogn. 2001;34(3):727–39.zbMATHGoogle Scholar
  35. 35.
    Manikandan A, Jamuna V. Single Image Super Resolution via FRI Reconstruction Method, Journal of Advanced Research in Dynamical and Control Systems. 2017; 23–28.Google Scholar
  36. 36.
    Moujahid A, Abanda A, Dornaika F. Feature Extraction Using Block-based Local Binary Pattern for Face Recognition, IS&T International Symposium on Electronic Imaging, Intelligent Robots and Computer Vision XXXIII: Algorithms and Techniques, Society for Imaging Science and Technology. 2016; 1–6.Google Scholar
  37. 37.
    Vijaya Kumar V, Srinivasa Reddy K, Venkata KV. Face recognition using prominent LBP Model,Intl. J Appl Eng Res. 2015;10(2):4373–84.Google Scholar
  38. 38.
    Rao VVK NG, PSVS R. Novel approaches of evaluating texture-based similarity features for efficient medical image retrieval system. International Journal Of Computer Applications. 2011;20(7):20–6.Google Scholar
  39. 39.
    Smith JR, Chang SF. Automated binary texture feature sets for image retrieval, Proc. IEEE Conf. Acoustics, Speech and Signal Processing, Columbia Univ. 1996; 2239–2242.Google Scholar
  40. 40.
    Local Binary Patterns (LBP) & Histogram of Oriented Gradient (HoG), http://biomisa.org/uploads /2016/10/Lect-15.pdf
  41. 41.
    Graña M, Termenon M, Savio A, Gonzalez-Pinto A, Echeveste J, Pérez JM. Computer aided diagnosis system for Alzheimer disease using brain diffusion tensor imaging features selected by Pearson’s correlation. Neurosci Lett. 2011;502(3):225–9.Google Scholar

Copyright information

© IUPESM and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Sanjeeva Polepaka
    • 1
    Email author
  • Ch. Srinivasa Rao
    • 2
  • M. Chandra Mohan
    • 3
  1. 1.Department of CSEMREC (A) & Research Scholar (JNTUH)SecunderabadIndia
  2. 2.Departments of ECEJNTUKUCEVVizianagaramIndia
  3. 3.Department of Computer Science and EngineeringJNTUCEH HyderabadHyderabadIndia

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