Segmentation of mammography by applying extreme learning machine in tumor detection

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

Volumn 7435 LNCS, Issue , 2012, Pages 92-100

  Cordeiro, F R ^a   Lima, S M L ^a   Silva Filho, A G ^a   Santos, W P ^a  

^a FEDERAL UNIVERSITY OF PERNAMBUCO   (Brazil)

Author keywords

Breast Cancer; Extreme Learning Machine; Neural Network; Segmentation

Indexed keywords

BREAST CANCER; CLASSIFICATION RATES; DIGITAL MAMMOGRAPHY; DIGITIZED MAMMOGRAMS; EXTREME LEARNING MACHINE; HARD TASK; LEARNING TIME; TRAINING AND TESTING; TUMOR DETECTION;

IMAGE SEGMENTATION; LEARNING SYSTEMS; MEDICAL IMAGING; NEURAL NETWORKS; TUMORS;

DIAGNOSIS;

EID: 84865036546     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-32639-4_12     Document Type: Conference Paper

References (15)

1. World Health Organization. The Global Burden of Disease: 2004 update (2008)
2. Breast cancer: prevention and control from WHO (World Health Organization), http://www.who.int/cancer/detection/breastcancer/en/index.html (accessed January 05, 2012)
3. Statistical data of breast cancer in Brazil from INCA (National Cancer Institute), http://www.inca.gov.br/estimativa/2008/ (accessed January 05, 2012)
4. Clark, G.M.: Prognostic and predictive factors. In: Harris, J.R., Lippmann, M.E. (eds.) Diseases of the Breast, 2nd edn., pp. 489-514. Lippincott Williams and Wilkins, Philadelphia (2000)
5. Litière, S., Werutsky, G., Fentiaman, I.S., et al.: Breast conserving therapy versus mastectomy for stage I-II breast cancer? 20 year follow-up of the EORTC 10901 phase 3 randomised trial. The Lancet Oncology 13(4), 412-419 (2012)
6. Costa, H., Solla, J., Temporão, J.G.: Controle do Câncer de Mama: Documento de Consenso. Ministério da Saúde, Secretária de Atenção à Saúde e Instituto Nacional de Câncer (2004)
7. Juhl, J.H., Crummy, A.B., Kuhlman, J.E.: Interpretação Radiológica. In: Koogan, G. (ed.), 7th edn. (2000)
8. Da-Xi, H., Yuan, F.: An Algorithm for Medical Imaging Identification based on Edge Detection and Seed Filling. In: Computer Application and System Modeling (ICCASM), pp. 547-548 (2010)
9. Ye, S., Zheng, S., Hao, W.: Medical Image Edge Detection Method based on Adaptive Facet Model. In: Computer Application and System Modeling (ICCASM), pp. 574-578 (2010)
10. Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: a new learning scheme of feedforward neural networks. In: Proceedings of International Joint Conference on Neural Networks (2004)
11. Jeatrakul, P., Wong, K.W.: Comparing The Performance of Different Neural Networks for Binary Classification Problems. In: Eighth International Symposium of Natural Language Processing (2009)
12. Andrioni, V., Guingo, B.C., Santana, E.L., Pereira, W.C.A., Infantosi, A.F.C.: Comparison of artificial neural networks using texture parameters in the recognition of lesions in mammograms digitized. In: 2011 Pan American of Health Care Exchanges (PAHCE), March 28-April 1, vol. 1, pp. 426-430 (2011)
13. Suckling, J., et al.: The Mammographic Image Analysis Society Digital Mammogram Database Exerpta Medica. International Congress Series 1069, 375-378 (1994)
14. Řiha, L., Mareboyana, M.: GPU accelerated one-pass algorithm for computing minimal rectangles of connected components. In: 2011 IEEE Workshop on Applications of Computer Vision (WACV), January 5-7, pp. 479-484 (2011)
15. Thomas, S.J., Cowley, I.R.: A Comparison of Four Indices for Combining Distance and Dose Differences. International Journal of Radiation Oncology*Biology*Physics 82, 717-723 (2012)