Algorithms for Computer Aided Diagnosis – An Overview
| ||International Journal of Computer Trends and Technology (IJCTT)|| |
|© - April Issue 2013 by IJCTT Journal|
|Volume-4 Issue-4 |
|Year of Publication : 2013|
|Authors :Dr.A.Padmapriya, K.Silamboli Chella Maragatham|
Dr.A.Padmapriya, K.Silamboli Chella Maragatham "Algorithms for Computer Aided Diagnosis – An Overview"International Journal of Computer Trends and Technology (IJCTT),V4(4):472-478 April Issue 2013 .ISSN 2231-2803.www.ijcttjournal.org. Published by Seventh Sense Research Group.
Abstract: -In medicine, two types of resources are becoming widely used: the Content-based Image Retrieval (CBIR) and the Computer- Aided Diagnosis (CAD) systems. The purpose of CAD is to increase the accuracy of diagnosis, as well as to improve the consistency of image interpretation by using the computer results as a second opinion. Similar to CAD systems, CBIR uses information extracted from images to represent them. However, the main purpose of a CBIR system is to retrieve ‘‘cases” or images similar to a given one. Analyzing past similar cases and their reports can improve the radiologist’s confidence on elaborating a new image report, besides making the training and the diagnosing process faster. Moreover, CAD and CBIR systems are very useful in medicine teaching. Currently, image mining has been focused by many researchers in data mining and information retrieval fields and has achieved prominent results. A major challenge in the image mining field is to effectively relate low-level features (automatically extracted from image pixels) to high-level semantics based on the human perception. Association rules has been successfully applied to other research areas, e.g. business, and can reveal interesting patterns relating low-level and high-level image data as well. In this work, association rules are employed to support both CAD and CBIR systems.
 M. Dash and H. Liu. Feature selection for classification. Intelligent Data Analysis — An International Journal, 1(3), 1997. http://www.public.asu.edu/_huanliu/papers/ida97.ps. 10, 12, 13, 14
 Juzhen Z. Dong, Ning Chong, and Setsuo Ohsuga. Using rough sets with heuristicsto feature selection. In Ning Zhong, Andrzej Skowron, and Setsuo Ohsuga, editors, Proceedings of the 7th International Workshop on New Directions in Rough Sets, Data Mining, and Granular-Soft Computing (RSFDGrC-99), volume 1711 of Lecture Notes inArtificial Intelligence, pages 178–187, Berlin, November 9–11 1999. Springer. 11, 21, 42.
 Rich Caruana and Dayne Freitag. Greedy attribute selection. In Proceedings of the 11thInternational Conference on Machine Learning, pages 28–36. Morgan Kaufmann, 1994. 10, 12, 14, 42, 43, 44.
 K. Kira and L. A. Rendell. The feature selection problem: Traditional methods and a new algorithm. In Proceedings of the Ninth National Conference on Artificial Intelligence, pages 129– 134. AAAI Press, 1992. 10
 George H. John, Ron Kohavi, and Karl Pfleger. Irrelevant features and the subset selection problem. In Proceedings of ICML-94, the Eleventh International Conference on Machine Learning, pages 121–129, New Brunswick, USA, 1994. 10 48
 Daphne Koller and Mehran Sahami. Toward optimal feature selection. In Proceedings of ICML-96, the Thirteenth International Conference on Machine Learning, pages 284–292, Bari, Italy, 1996. 10.
Keywords — Image mining, Association rules, Classification, Prediction.