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

Author(s): Bortolozzi, F. | Murshed, N. | Sabourin, R. |

Year: 1996

Citation: In Applications and Science of Artificial Neural Networks II - Proceeding of the SPIE, vol. 2760, pp. 487-494, Orlando, 1996.

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.

PDF download

Cross References