An improved medical image classification model using data mining techniques

In today's world, there is a dire need for the appropriate use of technology to diagnose and treat patients by analyzing medical data, which is usually in the form of images. This need calls for an in depth research in the field of data mining and its applications for medical treatments. In this paper, an improved method to classify medical images is discussed. This method encompasses concepts related to the k-nearest neighbor (kNN) Classification algorithm and concentrates on improving the prediction ability of the algorithm using weighting techniques. This paper also uses image pre-processing techniques to select the best representative features to classify an image and to avoid the curse of dimensionality. The improved KNN algorithm is modeled using pre-processed retinal fundus images. The performance of the proposed classifier is compared with the traditional kNN classifier using metrics such as classification accuracy and area under the ROC curve.

[1]  Osmar R. Zaïane,et al.  Application of Data Mining Techniques for Medical Image Classification , 2001, MDM/KDD.

[2]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[3]  Miguel E. Ruiz,et al.  Automatic Classification of Medical Images for Content Based Image Retrieval Systems (CBIR) , 2008 .

[4]  Neeraj Sharma,et al.  Segmentation and classification of medical images using texture-primitive features: Application of BAM-type artificial neural network , 2008, Journal of medical physics.

[5]  Vipin Kumar,et al.  Similarity Measures for Categorical Data: A Comparative Evaluation , 2008, SDM.

[6]  Miguel E Ruiz,et al.  Automatic medical image classification for content based image retrieval systems. , 2008, AMIA ... Annual Symposium proceedings. AMIA Symposium.

[7]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[8]  Abdul Rauf Baig,et al.  Using Association Rules for Better Treatment of Missing Values , 2006, ArXiv.

[9]  M. Madheswaran,et al.  Hybrid Medical Image Classification Using Association Rule Mining with Decision Tree Algorithm , 2010, ArXiv.

[10]  Andreas Uhl,et al.  Systematic Assessment of Performance Prediction Techniques in Medical Image Classification A Case Study on Celiac Disease , 2011, IPMI.

[11]  R. Bhavani,et al.  Classification of MRI brain images using k-nearest neighbor and artificial neural network , 2011, 2011 International Conference on Recent Trends in Information Technology (ICRTIT).

[12]  L Shaji,et al.  A Review of Medical Image Classification Techniques , 2011 .

[13]  Maryam Zekri,et al.  Review of Medical Image Classification using the Adaptive Neuro-Fuzzy Inference System , 2012, Journal of medical signals and sensors.

[14]  B. G. Prasad,et al.  Classification of Medical Images Using Data Mining Techniques , 2012 .

[15]  Bilwaj Gaonkar,et al.  Feature ranking based nested support vector machine ensemble for medical image classification , 2012, 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI).

[16]  J. Alamelu Mangai,et al.  A Novel Approach for Classifying Medical Images Using Data Mining Techniques , 2013 .