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Proceedings Paper

Classification of cancerous cells based on the one-class problem approach
Author(s): Nabeel A. Murshed; Flavio Bortolozzi; Robert Sabourin
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Paper Abstract

One of the most important factors in reducing the effect of cancerous diseases is the early diagnosis, which requires a good and a robust method. With the advancement of computer technologies and digital image processing, the development of a computer-based system has become feasible. In this paper, we introduce a new approach for the detection of cancerous cells. This approach is based on the one-class problem approach, through which the classification system need only be trained with patterns of cancerous cells. This reduces the burden of the training task by about 50%. Based on this approach, a computer-based classification system is developed, based on the Fuzzy ARTMAP neural networks. Experimental results were performed using a set of 542 patterns taken from a sample of breast cancer. Results of the experiment show 98% correct identification of cancerous cells and 95% correct identification of non-cancerous cells.

Paper Details

Date Published: 22 March 1996
PDF: 8 pages
Proc. SPIE 2760, Applications and Science of Artificial Neural Networks II, (22 March 1996); doi: 10.1117/12.235938
Show Author Affiliations
Nabeel A. Murshed, Centro Federal de Educacao Tecnologica do Parana (Brazil)
Flavio Bortolozzi, Centro Federal de Educacao Tecnologica do Parana (Brazil)
Robert Sabourin, Ecole de Technologie Superieure (Canada)


Published in SPIE Proceedings Vol. 2760:
Applications and Science of Artificial Neural Networks II
Steven K. Rogers; Dennis W. Ruck, Editor(s)

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