Applying Artificial Neural Network for the Classification of Breast Cancer Using Infrared Thermographic Images

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• 关键词：Image processing ; Thermography ; Breast cancer ; Artificial neural network
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9972
• 期：1
• 页码：429-438
• 全文大小：721 KB
• 参考文献：1.Ferlay, J., Soerjomataram, M., Ervik, M., Dikshit, R., Eser, S., Mathers, C., Rebelo, M., Parkin, D., Forman, D., Bray, F.: Cancer Incidence and Mortality Worldwide: IARC Cancer Base n 11 (2012). http://​globocan.​iarc.​fr
  2.Ng, E., Sudharsan, N.: Numerical computation as a tool to aid thermographic interpretation. J. Med. Eng. Technol. 25, 53–60 (2001)CrossRef
  3.Borchartt, T.: Thermographic Image Analysis for the Change of Classification in Breast. Univerisadade Federal Fluminense, Brasil (2013)
  4.Thomsen, L., Miles, D.: Happerfield: nitrie oxide synthase activity in human breast. Br. J. Cancer 72, 41 (1995)CrossRef
  5.Silva, L.F., Saade, D.C.M., Sequeiros, G.O., Silva, A.C., Paiva, A.C., Bravo, R.S., Conci, A.: A new database for breast research with infrared image. J. Med. Imaging Health Inf. 4(1), 92–100 (2014)CrossRef
  6.Canny, J.: A computational approach to edge detectiion. IEEE Trans. Pattern Anal. Mach. Intell. 6, 679–698 (1995)
  7.Nurhayati, O., Susanto, A., Sri Widodo, T., Tjokronagoro, M.: Principal component analysis combined with first order statistical method for breast thermal images classification. Int. J. Comput. Sci. Technol. (JCST) 2(2), 12–18 (2011)
  8.Freeman, J.A., Skapura, D.M.: Neural Networks: Algorithms, Applications, and Programming Techniques. Addison-Wesley, New York (1992)MATH
  9.Zhou, Q., Li, Z., Aggarwal, J.K.: Boundary extraction in thermal images by edge map. In: Proceedings of the ACM Symposium on Applied Computing, pp. 254–258 (2004)
  10.Haykin, S.: Neural networks: principles and practice. Trad. Paulo Martins Engel, Porto Alegre, Bookman, 2nd edn., p. 893 (2008)
  11.Koay, J., Herry, C., Frize, M.: Analysis of breast thermography with an artificial neural network. In: Conference of Proceedings IEEE Engineering in Medicine and Biology Society, vol. 2, p. 1159 (2004)
  12.Tang, X., Ding, H., Yuan, Y., Wang, Q.: Morphological measurement of localized temperature increase amplitudes in breast infrared thermograms and its clinical application. In: Biomedical Signal Processing and Control, vol. 3(4), p. 312, October 2008
  13.Arora, N., Martins, D., Ruggerio, D., Tousimis, E., Swistel, A., Osborne, M., Simmons, R.: Effectiveness of a noninvasive digital infrared thermal imaging system in the detection of breast cancer. Am. J. Surg. 196, 523–526 (2008)CrossRef
  14.Wishart, G.C., Campisi, M., Boswell, M., Chapman, D., Shackleton, V., Iddles, S., Hallett, A., Britton, P.D.: The accuracy of digital infrared imaging for breast cancer detection in women undergoing breast biopsy. Euro. J. Surg. Oncol. (EJSO) 36, 535–540 (2010)CrossRef
  15.Umadevi, V., Raghavan, S.V., Jaipurkar, S.: Interpreter for breast thermogram characterization. In: Conference on Biomedical Engineering and Sciences (IECBES), Kuala Lumpur, p. 150 (2010)
  16.Acharya, U.R., Ng, E.Y.K., Tan, J.H., Sree, S.V.: Thermography based breast cancer detection using texture features and support vector machine. J. Med. Syst. 36, 1503–1510 (2012)CrossRef
  17.Bishop, C.: Neural Networks for Pattern Recognition. Oxford University Press, Oxford (1995)MATH
  
• 作者单位：Vanessa Lessa (17)
  Mauricio Marengoni (17)
  
  17. Presbiterian University Mackenzie, São Paulo, 01302-907, Brazil
  
• 丛书名：Computer Vision and Graphics
• ISBN：978-3-319-46418-3
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：9972

文摘

The second type of cancer that kills more women in the world is breast cancer. If the prognosis is done at an early stage of the disease, women can have a better chance of cure. However, the access to medical exams in poor countries is usually precarious. This work describes the study of a computer-assisted diagnostic system using thermal imaging. The images are generated by a thermographic camera that has a lower cost than the equipment used in conventional exams. We propose a system that classifies the thermographic breasts images in “normal” and “abnormal”. We have analyzed 8 statistical characteristics: mean, variance, standard deviation (SD), skewness, kurtosis, entropy, range and median. The classification used an Artificial Neural Network (ANN) and got a result of 87 % in sensitivity, 83 % in specificity and 85 % in accuracy.