A Novel Biomedical Image Classification Using Kernel Support Vector Machine

References

[1] O. K. Firke, and Hemangi S. Phalak, “Brain Tumor Detection using CT Scan Images”, IJESC, Vol. 6, No. 8, pp. 2568-2570, August 2016.

[2] Suneetha Bobbillapati, and A. Jhansi Rani, “Automatic Detection of Brain Tumor through MRI”, International Journal of Scientific and Research Publication, Vol. 3, Issue 11, pp. 1-5, November 2013.

[3] D. Dilip Kumar, S Vandana, K. Sakhti Priya and S. Jeneeth Subhashini, “Brain Tumor Image Segmentation using MATLAB”, IJIRST, Vol. 1, Issue 12, pp. 447- 451, May 2015.

[4] A. Sivaramakrishnan, and Dr. M. Karnan, “A Novel Based Approach for extraction of Brain Tumor in MRI Images Using Soft Computing Techniques”, International Journal of Advanced Research in Computer and Communication Engineering, Vol. 2, Issue 4, April 2013.

[5] Riries Rulaningtyas and Khusnul Ain, “Edge Detection and Brain Tumor Pattern Recognition”, IEEE International Conference on Instrumentation, Communication, Information Technology and Biomedical Engineering, pp. 23- 25, Nov 2009.

[6] J. Wang, Y. Lu, J. Zang, Y. Li and B. Zang, “A Novel Approach for Segmentation of MRI Brain Images”, IEEE Mediterranean Electrotechnical Conference, pp. 325- 528, 2006.

[7] Ed Edily Mohd. Azari, Muhd. Mudzakkir Mohd. Hatta, Zaw Zaw Htike, and Shoon Lei Win, “Brain Tumor Detection and Localization in Magnetic Resonance Imaging”, IJITCS, Vol. 4, No.1, 2014.

[8] Jaskirat Kaur, Sunil Agarwal, and Renu Vig, “A Comparative Analysis of Thresholding and Edge Detection Segmentation Technique”, IJCA, Vol. 39, No. 15, February 2012.

[9] Riddhi S. Kapse, Dr. S.S. Salankar, Madhuri Babar, “Literature Survey on Detection of Brain Tumor from MRI Image”, IOSR-JECE, Vol. 10, Issue 1, pp. 80- 86, Jan- Feb 2015.

[10] https://en.wikipedia.org/wiki/Digital_image_processing.

@article{1702207,
      author = {K. Leela Prasad, K. Ganga Eswar, Lokesh. G., K. Sai Krishna},
      title = {A Novel Biomedical Image Classification Using Kernel Support Vector Machine},
      journal = {Iconic Research And Engineering Journals},
      year = {2020},
      volume = {3},
      number = {10},
      pages = {173-177},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/17022071.pdf},
      abstract = {An accurate and automated type classification of MRI scan based brain images is more extremely important during medical analysis and interpretation of brain. From over past decade various methods have already been implemented. In this Project, we can use a novel method for the classification of a given MRI brain scan image as normal or abnormal. The proposed method that was first perform wavelet transform to extract original features from MRI scanned images, In next step we can apply principle component analysis in order to reduce the dimensions of features. Those features are submitted to a kernel support vector machine (KSVM) for classification of Brain Images for normal and abnormal. The strategy of K-fold stratified cross validation was used to enhance generalization of KSVM.},
      keywords = {Tumor, Digital Signal Processing, Matlab, Gesture MRI Scan, Vector Machine, Kernel},
      month = {April},
  }