An Improved k Nearest Neighbor Classifier Using Interestingness Measures for Medical Image Mining
The exponential increase in the volume of medical image database has imposed new challenges to clinical routine in maintaining patient history, diagnosis, treatment and monitoring. With the advent of data mining and machine learning techniques it is possible to automate and/or assist physicians in clinical diagnosis. In this research a medical image classification framework using data mining techniques is proposed. It involves feature extraction, feature selection, feature discretization and classification. In the classification phase, the performance of the traditional kNN k nearest neighbor classifier is improved using a feature weighting scheme and a distance weighted voting instead of simple majority voting. Feature weights are calculated using the interestingness measures used in association rule mining. Experiments on the retinal fundus images show that the proposed framework improves the classification accuracy of traditional kNN from 78.57 % to 92.85 %.
[1] W. Hsu, M. L. Lee, and J. Zhang, “Image Mining : Trends &
Developments.” in J of Intellident Information Systems, vol 19, issue 1, ,
pp. 7-23, 2002..
[2] A. S. Elmaghraby, M. M. Kantardzic, and M P. Wachowiak, “Data
Mining from Multimedia Patient Records”, Data Mining and Knowledge
Discovery Approaches based on Rule Induction Techniques, Springer,
pp. 551–595, 2006.
[3] M.-L. Antonie, O. Zaiane, and A. Coman, “Application of Data Mining
Techniques for Medical Image Classification”, in Proc of 2nd Intl.
Workshop on Multimedia Data Mining, 2001, pp. 94-101.
[4] M. Vasantha, V. S. Bharathi and R. Dhamodaran, “Medical Image
Feature Extraction, Selection and Classification”, In. J of Engineering
Science and Technologl, vol. 2, no. 6, pp. 2071 – 2076, 2010
[5] P. Rajendran, and M. Madheswaran, “Hybrid Medical Image
Classification Using Association Rule Mining with Decision Tree
Algorithm”, J. of Computing, vol 2, no 1, pp. 127–136, 2010.
[6] A. Kharrat, K. Gasmi, M. B. Messaoud, N. Benamrane and M. Abid, “A
Hybrid Approach for Automatic Classification of Brain MRI Using
Genetic Algorithm and Support Vector Machine”, J. of Sciences, issue
17, pp. 71-82, 2010.
[7] B.G..Prasad and A. N. Krishna, “Classification of Medical Images using
Data Mining Techniques”, Advances in Communication, Network &
Computing”, Advances in Communication, Network & Computing,
Lecture Notes of the Institute of Computer sciences, social informatics
and Telecommunication Engineering, vol.108,, pp. 54-59, 2012.
[8] Z. S. Zubi, and R. A. Saad, “Using some Data Mining Techniques for
Early Diagnosis of Lung Cancer”, in Proc. Of 10th WSEAS Intl.
conference on Artificial Intelligence, Knowledge Engineering and Data
Bases, 2011, pp. 32-37.
[9] N. H. Rajini, and R.Bhavani, “Classification of MRI Brain Images using
k nearest neighbor and artificial neural network” IEEE-Intl conference
on Recent Trends in Information Technology, pp. 863-868, 2011.
[10] L. Muflikhah, and Adnyana, “Classifying Categorical Data Using
Modified K-Nearest Neighbor Weighted by Association Rules”, in Proc
of the Int Conf on Future Information Technology, vol 13, 2011,
pp. 347-351.
[11] J. Gou , L. Du , Y. Zhang and T. Xiong, “New Distance-weighted knearest
Neighbor Classifier”, J. of Informational and Computational
Science, vol 9, no 6, pp. 1426-1429, 2012.
[12] R. C. Ganzalez, amd R. E. Woods, Digital Image Processing, 2nd ed,
New Jersey: Prentice Hall 2001
[13] M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, I. H.
Witten. “The WEKA data mining software: an update”, ACM SIGKDD
Explorations, vol. 11, issue 1, pp. 10-18, June 2009.
[1] W. Hsu, M. L. Lee, and J. Zhang, “Image Mining : Trends &
Developments.” in J of Intellident Information Systems, vol 19, issue 1, ,
pp. 7-23, 2002..
[2] A. S. Elmaghraby, M. M. Kantardzic, and M P. Wachowiak, “Data
Mining from Multimedia Patient Records”, Data Mining and Knowledge
Discovery Approaches based on Rule Induction Techniques, Springer,
pp. 551–595, 2006.
[3] M.-L. Antonie, O. Zaiane, and A. Coman, “Application of Data Mining
Techniques for Medical Image Classification”, in Proc of 2nd Intl.
Workshop on Multimedia Data Mining, 2001, pp. 94-101.
[4] M. Vasantha, V. S. Bharathi and R. Dhamodaran, “Medical Image
Feature Extraction, Selection and Classification”, In. J of Engineering
Science and Technologl, vol. 2, no. 6, pp. 2071 – 2076, 2010
[5] P. Rajendran, and M. Madheswaran, “Hybrid Medical Image
Classification Using Association Rule Mining with Decision Tree
Algorithm”, J. of Computing, vol 2, no 1, pp. 127–136, 2010.
[6] A. Kharrat, K. Gasmi, M. B. Messaoud, N. Benamrane and M. Abid, “A
Hybrid Approach for Automatic Classification of Brain MRI Using
Genetic Algorithm and Support Vector Machine”, J. of Sciences, issue
17, pp. 71-82, 2010.
[7] B.G..Prasad and A. N. Krishna, “Classification of Medical Images using
Data Mining Techniques”, Advances in Communication, Network &
Computing”, Advances in Communication, Network & Computing,
Lecture Notes of the Institute of Computer sciences, social informatics
and Telecommunication Engineering, vol.108,, pp. 54-59, 2012.
[8] Z. S. Zubi, and R. A. Saad, “Using some Data Mining Techniques for
Early Diagnosis of Lung Cancer”, in Proc. Of 10th WSEAS Intl.
conference on Artificial Intelligence, Knowledge Engineering and Data
Bases, 2011, pp. 32-37.
[9] N. H. Rajini, and R.Bhavani, “Classification of MRI Brain Images using
k nearest neighbor and artificial neural network” IEEE-Intl conference
on Recent Trends in Information Technology, pp. 863-868, 2011.
[10] L. Muflikhah, and Adnyana, “Classifying Categorical Data Using
Modified K-Nearest Neighbor Weighted by Association Rules”, in Proc
of the Int Conf on Future Information Technology, vol 13, 2011,
pp. 347-351.
[11] J. Gou , L. Du , Y. Zhang and T. Xiong, “New Distance-weighted knearest
Neighbor Classifier”, J. of Informational and Computational
Science, vol 9, no 6, pp. 1426-1429, 2012.
[12] R. C. Ganzalez, amd R. E. Woods, Digital Image Processing, 2nd ed,
New Jersey: Prentice Hall 2001
[13] M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, I. H.
Witten. “The WEKA data mining software: an update”, ACM SIGKDD
Explorations, vol. 11, issue 1, pp. 10-18, June 2009.
@article{"International Journal of Medical, Medicine and Health Sciences:65031", author = "J. Alamelu Mangai and Satej Wagle and V. Santhosh Kumar", title = "An Improved k Nearest Neighbor Classifier Using Interestingness Measures for Medical Image Mining", abstract = "The exponential increase in the volume of medical image database has imposed new challenges to clinical routine in maintaining patient history, diagnosis, treatment and monitoring. With the advent of data mining and machine learning techniques it is possible to automate and/or assist physicians in clinical diagnosis. In this research a medical image classification framework using data mining techniques is proposed. It involves feature extraction, feature selection, feature discretization and classification. In the classification phase, the performance of the traditional kNN k nearest neighbor classifier is improved using a feature weighting scheme and a distance weighted voting instead of simple majority voting. Feature weights are calculated using the interestingness measures used in association rule mining. Experiments on the retinal fundus images show that the proposed framework improves the classification accuracy of traditional kNN from 78.57 % to 92.85 %.
", keywords = "Medical Image Mining, Data Mining, Feature Weighting, Association Rule Mining, k nearest neighbor classifier.", volume = "7", number = "9", pages = "554-5", }
