Data mining with decision trees and neural networks for calcification detection in mammograms

Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

Volumn 2972, Issue , 2004, Pages 232-241

  Flores, Beatriz A ^a   Gonzalez, Jesus A ^a  

^a INSTITUTO NACIONAL DE ASTROFÍSICA   (Mexico)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; AUTOMATION; BACKPROPAGATION; CALCIFICATION (BIOCHEMISTRY); COMPUTER VISION; DATA MINING; DECISION THEORY; NEURAL NETWORKS; ONCOLOGY; PROBLEM SOLVING; TUMORS; ARTIFICIAL INTELLIGENCE; BIOMINERALIZATION; BONE; DECISION TREES; FORESTRY; MAMMOGRAPHY; TREES (MATHEMATICS); X RAY SCREENS;

BREAST CANCER; DECISION TREES; NEURAL NETWORK TRAINING; AUTOMATIC TOOLS; BACK PROPAGATION NEURAL NETWORKS; CALCIFICATION DETECTIONS; DATA MINING TASKS; DOMAIN EXPERTS; MICROCALCIFICATIONS; PREVENTION MEASURES; SIMILAR PATTERN;

DIAGNOSTIC RADIOGRAPHY; CALCIFICATION (BIOCHEMISTRY);

EID: 9444251676     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/978-3-540-24694-7_24     Document Type: Conference Paper

References (11)

1. Antonie, Maria-Luisa, Zaiane, Osmar R., and Coman, Alexandru. Application of Data Mining Techniques for Medical Image Classification. Proceedings of the International Workshop on Multimedia Data Mining, San Francisco, CA. (2001) 94-101.
2. Bovis, K., Singh, S. Detection of Masses in Mammograms using Texture Measures. Fifteenth International Conference on Pattern Recognition. Barcelona. IEEE Press. vol 2 (2000) 267-270.
3. Bottigli, U. and Golosio, B. Feature Extraction from Mammographic Images Using Fast Matching Methods. Nuclear Instruments and Methods in Physics Research. ELSEVIER, A 487, (2002) 209-215.
4. Fayyad, Usama M., Gregory Piatetsky-Shapiro, and Padhraic Smyth. From Data Mining to Knowledge Discovery in Databases: An Overview. In Advances of Knowledge Discovery and Data Mining, eds. U.M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, AAAI Press/The MIT Press, Menlo Park, CA., (1996) 1-34.
5. López-Carrillo, Lizbeth; Torres-Sánchez, Luisa; López-Cervantes, Malaquías; and Rueda-Neria, Celina. Identification of Malignant Breast Lesions in Mexico. Salud Pública de Mexico. Vol. 43, No. 3, (2001) 199-202.
6. Quinlan, J. R. Improved Use of Continuous Attributes in C4.5, Journal of Artificial Intelligence Research, Vol. 4, (1996) 77-90.
7. Rumelhart, David E., Widrow, Bernard, and Lehr, Michael A. The Basic Ideas in Neural Networks. Communications of the ACM, Vol. 37, No. 3 (1994) 87-92.
8. th Australian and New Zeland Intelligent Information Systems Conference. Perth, November, (2001) 18-21.
9. Tabar L, Fagerbgerg G, Gad A, et al. Reduction in Mortality from Breast Cancer after Mass Screening with Mammography. Lancet. 1 (1985) 829-832.
10. Yoshida, H.; Doi, K.; Nishikawa, R.; Muto, K. and Tsuda, M. Application of the Wavelet Transform to Automated Detection of Clustered Microcalcifications in Digital Mammograms. Academic Reports of Tokyo Institute of Polytechnics, 16, (1994) 24-37.
11. Zhang, Xiao-Ping and Desai, Mita D. Wavelet Based Automatic Thresholding for Image Segmentation. In Proceedings of the ICIP'97 conference, Santa Barbara, CA. (1997) 26-29.