A statistical-genetic algorithm to select the most significant features in mammograms

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4673 LNCS, Issue , 2007, Pages 189-196

  Sánchez Ferrero, Gonzalo V ^a   Arribas, Juan Ignacio ^a  

^a UNIVERSITY OF VALLADOLID   (Spain)

Author keywords

Breast cancer; Feature selection; Genetic algorithms; Medical diagnosis; Microcalcification classification; Neural network classifiers

Indexed keywords

FEATURE EXTRACTION; GENETIC ALGORITHMS; MAMMOGRAPHY; NEURAL NETWORKS; TUMORS;

BREAST CANCER; MICROCALCIFICATION CLASSIFICATION; NETWORK COMPLEXITY; NEURAL NETWORK CLASSIFIERS;

DIAGNOSIS;

EID: 38149142403     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-74272-2_24     Document Type: Conference Paper

References (18)

1. Zhou, X., Gordon, R.: Detection of early breast cancer: an overview and future prospects. Crit. Rev. Biomed. Eng. 17(3), 203-255 (1989)
2. Jiang, Y., Nishikawa, R.M., Wolverton, D.E., Metz, C.E., Giger, M.L., Schmidt, R.A., Vyborny, C.J., Doi, K.: Malignant and bening clustered microcalcifications: Automated feature analysis and classification. Radiology 198(3), 671-678 (1996)
3. Haralick, R.M.: Statistical and structural approaches to texture. In: Proceedings of the IEEE. vol. 67(5), pp. 786-804 (1979)
4. Clausi, D.A., Jernigan, M.E.: A fast method to determine co-occurrence texture features. IEEE trans. geosci. remote sens. 36(1), 298-300 (1998)
5. Verma, B., Zhang, P.: A novel neural-genetic algorithm to find the most significant combination of features in digital mammograms. Appl. Soft Comput. 7(2), 612-625 (2007)
6. Liu, H., Yu, L.: Toward integrating feature selection algorithms for classification and clustering. IEEE Transactions on Knowledge and Data Engineering 17(4), 491-502 (2005)
7. Langley, P.: Selection of Relevant Features in Machine Learning. In: Proc. AAAI Fall Symp. Relevance, pp. 140-144 (1994)
8. Liu, H., Motoda, H.: Feature Selection for Knowledge Discovery and Data Mining. Kluwer Academic Publishers, Boston, MA (1998)
9. Arribas, J.I., Cid-Sueiro, J.: A Model Algorithm for a Posteriori Probability Estimation With Neural Networks. IEEE Trans. on Neural Netw. 16(4), 799-809 (2005)
10. Suckling, J., et al.: The mammographic image analysis society digital mammogram database. Exerpta Medica. International Congress Series 1069, 375-378 (1994)
11. Jain, A.K., Duin, R., Mao, J.: Statistical Pattern Recognition: A Review. IEEE Trans. on Pattern Analysis and Machine Intelligence 22(1), 4-37 (2000)
12. Zahn, C.T., Roskies, R.Z.: Fourier descriptors for plane closed curves. IEEE Trans. on Computing C-21(3), 269-281 (1972)
13. Chuang, G.C.-H., Kuo, C.-C.J.: Wavelet descriptor of planar curves: theory and applications. IEEE Trans. on Image Proc. 5(1), 56-70 (1996)
14. Jain, A.K.: Fundamentals of digital image processing. Prentice-Hall, Englewood Cliffs (1989)
15. Ghosal, S., Mehrotra, R.: A moment-based unified approach to image feature detection. IEEE Trans. on Image Processing 6(6), 781-793 (1997)
16. Pierre, A.D., Kittler, J.: Pattern recognition: a statistical approach. Prentice/Hall International, Englewood Cliffs (1982)
17. Hong, J., Cho, S.: Efficient huge-scale feature selection with speciated genetic algorithm. Pattern Recogn. Lett. 27(2), 143-150 (2006)
18. Tang, K.S., Man, K.F., Kwong, S., He, Q.: Genetic algorithms and their applications. IEEE Signal Processing Magazine 13(6), 22-37 (1996)