The week's pick
Random Articles
Reseach Article
Artificial Neural Network based Brain Cancer Analysis and Classification
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 66 - Number 10 |
| Year of Publication: 2013 |
| Authors: Aniket A. Kathalkar, R. S. Kawitkar, Amruta Chopade |
10.5120/11124-6087
|
Aniket A. Kathalkar, R. S. Kawitkar, Amruta Chopade . Artificial Neural Network based Brain Cancer Analysis and Classification. International Journal of Computer Applications. 66, 10 ( March 2013), 40-43. DOI=10.5120/11124-6087
Abstract
A Brain Cancer is very serious disease causing deaths of many individuals. The detection and classification system must be available so that it can be diagnosed at early stages. Cancer classification has been one of the most challenging tasks in clinical diagnosis. At present cancer classification is done mainly by looking through the cells' morphological differences, which do not always give a clear distinction of cancer subtypes. Unfortunately, this may have a significant impact on the final outcome of whether a patient could be cured effectively or not. This paper deals with such a system which uses computer based procedures to detect tumor blocks and classify the type of tumor using Artificial Neural Network Algorithm for MRI images of different patients. Different image processing techniques such as histogram equalization, image segmentation, image enhancement, morphological operations and feature extraction are used for detection of the brain tumor in the MRI images of the cancer affected patients.
References
- Dhawan, A. P. , "A Review on Biomedical Image Processing and Future Trends," Computer Methods and Programs in Biomedicine, Vol. 31, No. 3-4, 1990, pp. 141-183.
- Gonzalez, R. C. ; Woods, R. E. , Digital Image Processing, 2nd ed. , Prentice-Hall, Englewood Cliffs, NJ, 2002.
- Pal, N. R. and S. K. Pal, "A review on image segmentation techniques," Pattern Recognition 26(9): 1277-1294, 1993.
- Sonka, M. Hlavac, V. Boyle, R. (2004). Image processing, Analysis, and Machine Vision, II Edition, Vikas Publishing House, New Delhi.
- S. N. Sivanandam, S. Sumathi, S. N. Deepa, "Introduction to Neural Networks using Matlab 6. 0". Tata McGraw Hill Company Ltd, New Delhi, June 2005.
- Zhu H, Francis HY, Lam FK, Poon PWF. Deformable region model for locating the boundary of brain tumors. In: Proceedings of the IEEE 17th Annual Conference on Engineering in Medicine and Biology 1995. Montreal, Quebec, Canada: IEEE,1995; 411
- T. K. Yin and N. T. Chiu, "A computer-aided diagnosis for locating abnormalities in bone scintigraphy by fuzzy system with a three-step minimization approach," IEEE Trans. Med. Imaging, vol. 23, no. 5, pp. 639–654, 2004.
- X. Descombes, F. Kruggel, G. Wollny, and H. J. Gertz, "An object-based approach for detecting small brain lesions: Application to Virchow-robin spaces," IEEE Trans Med. Imaging, vol. 23, no. 2, pp. 246–255, 2004.
- Cline HE, Lorensen E, Kikinis R, Jolesz F. Threedimensional segmentation of MR images of the head using probability and connectivity. J Computer Assist Tomography 1990; 14:1037–1045.
- Vannier MW, Butterfield RL, Rickman DL, Jordan DM, Murphy WA, Biondetti PR. Multispectral magnetic resonance image analysis. Radiology 1985; 154:221–224.
- Jaceck Zurada, "Introduction to Artificial neural systems," Jaico Publishing pages 790.
- Simon Haykin, "Neural Network Design. " (2004). I Edition, Vikas Publishing House, New Delhi, India pp. 938.
Index Terms
Keywords