International Journal of Soft Computing

Year: 2013
Volume: 8
Issue: 3
Page No. 199 - 206

Comparative Analysis of Classifier Performance on Medical Image Diagnosis

Authors : Akila and Uma Maheswari

References

Dhanalakshmi, K. and V. Rajamani, 2010. An efficient association Rule-based method for diagnosing ultrasound kidney images. Proceedings of the Conference on Computational Intelligence and Computing Research, December 28-29, 2010, Coimbatore, pp: 1-5.

El-Far, M., L. Moumoun, M. Chahhou, T. Gadi and R. Benslimane, 2011. Comparing between data mining algorithms: Close+, Apriori and CHARM and Kmeans classification algorithm and applying them on 3D object indexing. Proceedings of the International Conference on Multimedia Computing and Systems, April 7-9, 2011, Ouarzazate, Morocco, pp: 1-6.

Flusser, J., 2005. Moment invariants in image analysis. World Acad. Sci. Eng. Technol., 11: 376-381.
Direct Link  |  

Ion, A.L. and S. Udristoiu, 2011. An experimental framework for learning the medical image diagnosis. Proceedings of 33rd International Conference on Information Technology Interfaces, June 27-30, 2011, Dubrovnik, pp: 465-470.

Jose, J.S., R. Sivakami, N.U. Maheswari and R. Venkatesh, 2012. An efficient diagnosis of kidney images using association rules. Int. J. Comput. Technol. Electron. Eng., 2: 14-20.
Direct Link  |  

Kharat, K.D., P.P. Kulkarni and M.B. Nagori, 2012. Brain tumor classification using neural network based methods. Int. J. Comput. Sci. Inform., 1: 2231-5292.
Direct Link  |  

Li, W., Z. Lu, Q. Feng and W. Chen, 2010. Meticulous classification using support vector machine for brain images retrieval. Proceedings of the International Conference on Medical Image Analysis and Clinical Application, June 10-13, 2010, Guangdong, pp: 99-102.

Olukunle, A. and S.A. Ehikioya, 2002. A fast algorithm for mining association rules in medical image data. Electr. Comput. Eng., 2: 1181-1187.
CrossRef  |  

Rajendran, P. and M. Madheswaran, 2009. An improved image mining technique for brain tumour classification using efficient classifier. Int. J. Comput. Sci. Inform. Secur., 6: 107-116.
Direct Link  |  

Rajendran, P. and M. Madheswaran, 2009. Pruned associative classification technique for the medical image diagnosis system. Proceedings of the 2nd International Conference on Machine Vision, December 28-30, 2009, Dubai, pp: 293-297.

Ribeiro, M.X., A.J. M. Traina, C. Traina and P.M. Azevedo-Marques, 2008. An association Rule-based method to support medical image diagnosis with efficiency. IEEE Trans. Multimedia, 10: 277-285.
CrossRef  |  

Shekhawat, P.B. and S.S. Dhande, 2011. A classification technique using associative classification. Int. J. Comput. Appli., 20: 21-28.
Direct Link  |  

Shekhawat, P.B. and S.S. Dhande, 2011. Building an iris plant data classifier using neural network associative classification. Int. J. Adv. Technol., 2: 491-506.
Direct Link  |  

Torres, R.S. and A.X. Falcao, 2006. Content-based image retrieval: Theory and applications. Revista Informatica Teorica E Aplicada, 13: 161-185.

Zacharaki, E.I., S. Wang, S. Chawla, D.S. Yoo, R. Wolf, E.R. Melhem and C. Davatzikos, 2009. Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme. Magn. Reson. Med., 62: 1609-1618.
CrossRef  |  Direct Link  |  

Zaiane, O.R., M.L. Antonie and A. Coman, 2002. Mammography classification by an association rule based classifier. Proceedings of the International Workshop on Multimedia Data Mining with ACM SIGKDD, July 23, 2002, Edmonton, Alberta, Canada, pp: 62-69.

Design and power by Medwell Web Development Team. © Medwell Publishing 2024 All Rights Reserved