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A Diagnostic Feature Subset Selection of Breast Tumor Based on Neighborhood Rough Set Model  

Son, Chang-Sik (DGIST 웰니스융합연구센터)
Choi, Rock-Hyun (DGIST 웰니스융합연구센터)
Kang, Won-Seok (DGIST 웰니스융합연구센터)
Lee, Jong-Ha (계명대학교 의과대학 의용공학과)
Publication Information
Journal of the Korea Industrial Information Systems Research / v.21, no.6, 2016 , pp. 13-21 More about this Journal
Feature selection is the one of important issue in the field of data mining and machine learning. It is the technique to find a subset of features which provides the best classification performance, from the source data. We propose a feature subset selection method using the neighborhood rough set model based on information granularity. To demonstrate the effectiveness of proposed method, it was applied to select the useful features associated with breast tumor diagnosis of 298 shape features extracted from 5,252 breast ultrasound images, which include 2,745 benign and 2,507 malignant cases. Experimental results showed that 19 diagnostic features were strong predictors of breast cancer diagnosis and then average classification accuracy was 97.6%.
Neighborhood Rough Set; Neighborhood Approximations; Feature Selection; Breast Tumor Diagnosis;
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Times Cited By KSCI : 3  (Citation Analysis)
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1 F. Bessaoud, J. P. Daures, “Dietary Factors and Breast Cancer Risk: A Case Control Study Among a Population in Southern France,” Nutrition and Cancer, Vol. 60, No. 2, pp. 177-187, 2008.   DOI
2 Yoo, Y. G., Choi, S. K., Hwang, S. J., and Kim, H. S., “Risk Factors of Breast Cancer According to Life Style,” Journal of The Korea Contents Association, Vol. 13, No. 4, pp. 262-272, 2013.
3 Nam, S. J., “Screening and Diagnosis for Breast Cancers,” Journal of The Korean Medical Association, Vol. 52, No. 10, pp. 946-951, 2013.   DOI
4 Chung, S. Y. and Han. B. K., "Breast Diagnostic Imaging," Seoul, Ilchokak, 2006.
5 Bae, J. M., “On the Benefits and Harms of Mammography for Breast Cancer Screening in Korean Woman,” Korean Journal of Family Practice, Vol. 4, No. 1, pp. 1-6, 2014.
6 Berg, W. A., “Rationale for a Trial of Screening Breast Ultrasound: American College of Radiology Imaging Network (ACRIN) 6666,” American Journal of Roentgenology, Vol. 180, No. 5, pp. 1225-1228, 2003.   DOI
7 Berg, W. A., “Supplemental Screening Sonography in Dense Breasts,” Radiologic Clinics of North America, Vol. 42, No. 5, pp. 845-851, 2004.   DOI
8 Berg, W. A., “Beyond Standard Mammographic Screening: Mammography at Age Extremes, Ultrasound, and MR Imaging,” Radiologic Clinics of North America, Vol. 45, No. 5, pp. 895-906, 2007.   DOI
9 Berg, W. A., Blume, J. D., and Cormack, J. B., “Combined Screening with Ultrasound and Mammography vs. Mammography Alone in Women at Elevated Risk of Breast Cancer,” JAMA, Vol. 299, No. 18, pp. 2151-2163, 2008.   DOI
10 Lim, S. Y., Lee, S. J., Shin, Y. K., Lee, S. N., Choi, J. Y., and Kang, D. R., “Comparison of the Diagnostic Value Between Mammography and Mammography with Breast Ultrasonography in Diagnosing Breast Cancer,” Korean Journal of Family Medicine, Vol. 24, No. 10, pp. 925-933, 2003.
11 Jang, J. M., Yi, A., and Koo, H. R., “Differentiation of Breast Mass Using Automated Breast US: Application of US BI-RADS lexicon,” Journal of The Korean Society for Breast Screening, Vol. 8, No. 2, pp. 115-120, 2011.
12 Giger, M. L., Chan, H. P., and Boone, J. "Anniversary Paper: History and Status of CAD and Quantitative Image Analysis: The Role of Medical Physics and AAPM," Medical Physics, Vol. 35, No. 12, pp. 5799-5820, 2008.   DOI
13 Guo, Y. H., Cheng, H. D., Huang, J. H., Tai, J. W., Zhao, W., and Sun, L., “Breast Ultrasound Image Enhancement Using Fuzzy Logic,” Ultrasound in Medicine and Biology, Vol. 32, No. 2, pp. 237-247, 2006.   DOI
14 Cheng, J. Z., Chou, Y. H., C. S., Huang, Chang, Y. C., Tiu, C. M., and Chen, K. W., "Computer-Aided US Diagnosis of Breast Lesions by Using Cell-Based Contour Grouping," Radiology, Vol. 255, No. 3 pp. 746-754, 2010.
15 Sahiner, B., “Computer-Aided Characterization of Mammographic Masses: Accuracy of Mass Segmentation and its Effects on Characterization,” IEEE Transactions on Medical Imaging, Vol. 20, No. 12, pp. 1275-1284, 2001.   DOI
16 Setiono, R., "Generating Concise and Accurate Classification Rules for Breast Cancer Diagnosis," Artificial Intelligence in Medicine, Vol 18, No.3, pp. 205-217, 2000.   DOI
17 Joo, S., Yang, Y. S., Moon, W. K., and Kim, H. C., “Computer-Aided Diagnosis of Solid Breast Nodules: Use of an Artificial Neural Network Based on Multiple Sonographic Feature,” IEEE Transactions on Medical Imaging, Vol. 23, No. 10, pp. 1292-1300, 2004.   DOI
18 Lee, J. H., Seong, Y. K., Chang, C. H., Park, J. M., Park, M. H., and Woo, K. G., "Fourier-Based Shape Feature Extraction Technique for Computer-Aided B-Mode Ultrasound Diagnosis of Breast Tumor," 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), August 28-September 1, San Diego, California, USA, pp. 6551-6554, 2012.
19 Abonyi, J. and Szeifert, F., “Supervised Fuzzy Clustering for the Identification of Fuzzy Classifiers,” Pattern Recognition Letters, Vol. 14, No. 24, pp. 2195-2207, 2003.
20 Goodman, D. E., Boggess, L., and Watkins, A. "Artificial Immune System Classification of Multiple-Class Problems," In Proceedings of Intelligent Engineering Systems, pp. 179-184, 2002.
21 Akay, M. F., “Support Vector Machines Combined with Feature Selection for Breast Cancer Diagnosis,” Expert Systems with Applications, Vol. 36, No. 2, pp. 3240-3247, 2009.   DOI
22 Lee, J. H., Seong, Y. K., Chang, C. H., Ko, E. Y., Cho, B. H., and Ku, J. H., "Computer-Aided Lesion Diagnosis in B-Mode Ultrasound by Border Irregularity and Multiple Sonographic Features," Proceedings of SPIE, Medical Imaging 2013, February 2013.
23 Hu, Q. H., Yu, D. R., and Xie, Z. X., “Neighborhood Classifiers,” Expert Systems with Applications, Vol. 34, No. 2, pp. 866-876, 2008.   DOI
24 Hu, Q. H., Yu, D. R., Liu, J. F., and Wu, C. X., “Neighborhood Rough Set Based Heterogeneous Feature Subset Selection,” Information Sciences, Vol. 178, No. 18, pp. 3577-3594, 2008.   DOI
25 Ha, S. H. and Zhang, Z. Y., “Empirical Evaluation of Ensemble Approach for Diagnostic Knowledge Management,” The Journal of Information Systems, Vol. 20, No. 3, pp. 237-255, 2011.   DOI
26 Son, C. S., Kang, W. S., Choi, R. H., Park, H. S., Han, S. W., and Kim, Y. N., “A Probabilistic Knowledge Model for Analyzing Heart Rate Variability,” Journal of the Korea Industrial Systems Research, Vol. 20, No. 3, pp. 61-69, 2015.