Classification of cancerous cells based on the one-class problem approach

Nabeel A. Murshed; Flavio Bortolozzi; Robert Sabourin

doi:10.1117/12.235938

22 March 1996 Classification of cancerous cells based on the one-class problem approach

Nabeel A. Murshed, Flavio Bortolozzi, Robert Sabourin

Proceedings Volume 2760, Applications and Science of Artificial Neural Networks II; (1996) https://doi.org/10.1117/12.235938
Event: Aerospace/Defense Sensing and Controls, 1996, Orlando, FL, United States

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.

Citation Download Citation

Nabeel A. Murshed, Flavio Bortolozzi, and Robert Sabourin "Classification of cancerous cells based on the one-class problem approach", Proc. SPIE 2760, Applications and Science of Artificial Neural Networks II, (22 March 1996); https://doi.org/10.1117/12.235938

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