뉴럴네트워크 기반의 유방암 조기 진단을 위한 분류 = Classification for early diagnosis for breast cancer base on Neural Network

국문 초록 (Abstract)

유방암은 전체 여성의 암환자 중 두 번째로 많으며, 여성의 암으로 인한 사망 원인으로 가장 높은 것으로 나타났다. 유방암은 조기 발견 경우 완치율이 92%에 이른다. 하지만, 조기 발견을 하지 못할 경우 유방암은 전이 율이 매우 높다. 암세포의 전이는 암의 진행이 많이 될수록 다른 장기로의 전이가 더욱 잘 되는 것으로 나타났다. 암의 조기 진단은 삶의 질을 높일 수 있는 중요한 요소이다. 유방암을 검사하는 방법으로는 맘모그래피(Mammography), 초음파, 맘모톰(momotome) 등이 있다. 그 중 맘모그래피는 검사자에게 통증이 적을 뿐 아니라, 쉽게 접근할 수 있어 유방암 검사에 유용하게 사용된다. 본 논문에서는 유방암 진단 데이터로 맘모그래프 데이터를 사용하였다. 본 논문에서는 뉴럴네트워크인 NEWFM(Neural network with weighted fuzzy membership function)를 사용하여 암 조기 진단을 위한 클래스를 분류하였다. NEWFM을 이용하여 데이터를 학습시킨 후 유방암 데이터 분류 결과 정확도가 84.4391%가 나타났다.

다국어 초록 (Multilingual Abstract)

Breast cancer is the sccond most female cancer patient in the entire female cancer patient, and has emerged as the highest contributor to female cancer deaths. If breast cancer id detected early, the cure rate is 92 percent. However, if early detection fails, breast cancer has a very high rate of metastasis. The transition from cancer to cancer has become more successful as cancer progresses. Early diagnosis of cancer is an important factor in improving quality of life. Examples of breast cancer include Mammograph, ultrasound, and Momotome. Mommography is not only painful for the examiner, but also for easy access to breast cancer exam inations. In this paper, breast cancer diagnosis data mammograph data was used. In addition, the Neural Network were classified for early diagnosis of breast cancer early using NEWFM. After learning of data using NEWFM, the accuracy of the breast cancer data classification was 84.4391%.

참고문헌 (Reference)

1 "www.kmpnews.co.kr"

2 "http://www.kbcs.or.kr"

3 Markey, M. K, "and breast cancer diagnosis" 32 : 99-109, 2002

4 "UCIMachine Learning Repository"

5 Wlter, M, "The prediction breast cancer biopsy outcomes using two DAD approaches that both emphasize an intelligible decision process" 34 (34): 4164-4172, 2007

6 Edge SB, "The American Joint Committee on Cancer: the 7th edition of the AJCC cancer staging manual and the future of TNM"

7 H. J. Yoon, "Prediction of Time Series Microarray Data using Neurofuzzy Network" 8 (8): IPL0485-, 2015

8 Ismail Saritas, "Prediction of Breast Cancer Using Artificial Neural Networks" 201236 : 2901-2907,

9 S. H. Lee, "Parkinson’s disease classification using gait characteristics and wavelet-based feature extraction" 39 : 7338-7344, 2012

10 S. H. Lee, "Parkinson’s disease classification using gait characteristics and wavelet-based feature extraction" 39 : 7338-7344, 2012

11 H. J. Yoon, "Neural Network based Design for classifying breast cancer pathway" 7 (7): 2017

12 S.H. Lee, "Forecasting KOSPI based on a neural network with weighted fuzzy membership function" 39 : 4259-4263, 2011

13 Floyd, C.E, "Case-based reasoning computer algorithm that uses mammographic findings for breast biopsy decision" 175 : 1347-1352, 2000

14 American College of Radiology, "Breast imaging reporting and data system (BI-RADS)" American College of Radiology 2003

15 Baker, J. A, "Breast cancer: Prediction with artificial neural network based on BI-RADS standardized lexicon" 196 : 817-822, 1995