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A Fully Automated Breast Cancer Recognition System Using Discrete-Gradient Based Clustering and Multi Category Feature Selection

journal contribution
posted on 2023-11-06, 00:54 authored by R Ghosh, M Ghosh, John YearwoodJohn Yearwood
Advances in machine intelligence have provided a whole new window of opportunities in medical research. Building a fully automated computer aided diagnostic system for digital mammograms is just one of them. Given some success with semi-automated systems earlier, a fully automated CAD system is just another step forward. A proper combination of a feature selection model and a classifier for those areas of a mammogram marked by radiologists has been very successful. However a fully automated system with only two modules is a time consuming process as the suspicious areas in a mammogram can be quite small when compared to the whole image. Thus an additional clustering process can help in reducing the time complexity of the overall process. In this paper we propose a fast clustering process to identify suspicious areas. Another novelty of this paper is a multi-category feature selection approach. The choice of features to represent the patterns affects several aspects of pattern recognition problems such as accuracy, required learning time and the required number of samples. In this paper we propose a hybrid canonical based feature extraction technique as a combination of an evolutionary algorithm based classifier with a feed forward MLP model.

History

Journal

Journal of Advanced Computational Intelligence and Intelligent Informatics

Volume

9

Pagination

244-256

ISSN

1343-0130

eISSN

1883-8014

Language

en

Issue

3

Publisher

Fuji Technology Press Ltd.

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