Medical Diagnosis System of Breast Cancer Using FCM Based Parallel Neural Networks

Authors:
Sang-Hyun Hwang;Dongwon Kim;Tae-Koo Kang;Gwi-Tae Park
Affiliations:
Department of Electrical Engineering, Korea University, 1, 5-ka, Anam-dong, Seongbuk-ku, Seoul 136-701, Korea;Department of Electrical Engineering, Korea University, 1, 5-ka, Anam-dong, Seongbuk-ku, Seoul 136-701, Korea;Department of Electrical Engineering, Korea University, 1, 5-ka, Anam-dong, Seongbuk-ku, Seoul 136-701, Korea;Department of Electrical Engineering, Korea University, 1, 5-ka, Anam-dong, Seongbuk-ku, Seoul 136-701, Korea
Venue:
ICIC '07 Proceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence
Year:
2009

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Abstract

In this paper, a new methodology for medical diagnosis based on fuzzy clustering and parallel neural networks is proposed. Intelligent systems have various fields. Breast cancer is one of field to be targeted, which is the most common tumor-related disease among women. Diagnosis of breast cancer is not task for medical expert owing to many attributes of the disease. So we proposed a new method, FCM based parallel neural networks to handle difficult. FCM based parallel neural networks composed of two parts. One is classifying breast cancer data using Fuzzy c-means clustering method (FCM). The other is designing the multiple neural networks using classified data by FCM. The proposed methodology is experimented, evaluated, and compared the performance with other existed models. As a result we can show the effectiveness and precision of the proposed method are better than other previous models.