Breast tumor classification in ultrasound images using support vector machines and neural networks
Nascimento, Carmina Dessana Lima; Silva, Sérgio Deodoro de Souza; Silva, Thales Araújo da; Pereira, Wagner Coelho de Albuquerque ; Costa, Marly Guimarães Fernandes; Costa Filho, Cícero Ferreira Fernandes
http://dx.doi.org/10.1590/2446-4740.04915
Res. Biomed. Eng., vol.32, n3, p.283-292, 2016
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Views: 986
Abstract
Introduction: The use of tools for computer-aided diagnosis (CAD) has been proposed for detection and classification of breast cancer. Concerning breast cancer image diagnosing with ultrasound, some results found in literature show that morphological features perform better than texture features for lesions differentiation, and indicate that a reduced set of features performs better than a larger one. Methods: This study evaluated the performance of support vector machines (SVM) with different kernels combinations, and neural networks with different stop criteria, for classifying breast cancer nodules. Twenty-two morphological features from the contour of 100 BUS images were used as input for classifiers and then a scalar feature selection technique with correlation was used to reduce the features dataset. Results: The best results obtained for accuracy and area under ROC curve were 96.98% and 0.980, respectively, both with neural networks using the whole set of features. Conclusion: The performance obtained with neural networks with the selected stop criterion was better than the ones obtained with SVM. Whilst using neural networks the results were better with all 22 features, SVM classifiers performed better with a reduced set of 6 features.
Keywords
Breast tumors, Breast ultrasound images, Neural network, Support vector machine.
References
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Alvarenga AV, Infantosi AFC, Pereira WCA, Azevedo CM. Assessing the performance of morphological parameters in distinguishing breast tumors on ultrasound images. Medical Engineering & Physics. 2010; 32(1):49-56. PMid:19926514. http://dx.doi.org/10.1016/j.medengphy.2009.10.007.
Alvarenga AV, Infantosi AFC, Pereira WCA, Azevedo CM. Assessing the combined performance of texture and morphological parameters in distinguishing breast tumors in ultrasound images. Medical Physics. 2012; 39(12):7350-8. PMid:23231284. http://dx.doi.org/10.1118/1.4766268.
Alvarenga AV, Pereira WCA, Infantosi AFC, Azevedo CM. Classification of breast tumours on ultrasound images using morphometric parameters. In: Ruano MG, Ruano AE, editors. Proceedings of the International Workshop on Intelligent Signal Processing; 2005 Sept 1-3; Algarve, Portugal. Piscataway: IEEE; 2005. p. 206-10.
Chang RF, Wu W-J, Moon WK, Chen D-R. Improvement in breast tumor discrimination by support vector machines and speckle-emphasis texture analysis. Ultrasound in Medicine & Biology. 2003; 29(5):679-86. PMid:12754067. http://dx.doi.org/10.1016/S0301-5629(02)00788-3.
Chen C-M, Chou Y-H, Han K-C, Hung G-S, Tiu C-M, Chiou H-J, Chiou SY. Breast lesions on sonograms: computer-aided diagnosis with nearly setting-independent features and artificial neural networks. Radiology. 2003; 226(2):504-14. PMid:12563146. http://dx.doi.org/10.1148/radiol.2262011843.
Chen D-R, Chang R-F, Chen C-J, Ho M-F, Kuo S-J, Chen S-T, Hung S-J and Moon W-K. Classification of breast ultrasound images using fractal feature. Clinical Imaging. 2005; 29(4):235-45.
Chen D-R, Chang R-F, Huang Y-L. Computer-aided diagnosis applied to us of solid breast nodules by using neural networks. Radiology. 1999; 213(2):407-12. PMid:10551220. http://dx.doi.org/10.1148/radiology.213.2.r99nv13407.
Cheng HD, Shan J, Ju W, Guo Y, Zhang L. Automated breast cancer detection and classification using ultrasound images: a survey. Pattern Recognition. 2010; 43(1):299-317. http://dx.doi.org/10.1016/j.patcog.2009.05.012.
Chiang HK, Tiu C-M, Hung G-S, Wu S-C, Chang TY, Chou Y-H. Stepwise logistic regression analysis of tumor contour features for breast ultrasound diagnosis. In: IEEE International Ultrasonics Symposium (IUS); 2001; Atlanta, USA. New York: IEEE; 2001. v. 2, p. 1303-6.
Dennis MA, Parker SH, Klaus AJ, Stavros AT, Kaske TI, Clark SB. Breast biopsy avoidance: The value of normal mammograms and normal sonograms in the setting of a palpable lump. Radiology. 2001; 219(1):186-91. PMid:11274555. http://dx.doi.org/10.1148/radiology.219.1.r01ap35186.
Doan CD, Liong S-Y, editors. Generalization for multilayer neural network: Bayesian regularization or early stopping. In: Proceedings of the 2nd Conference of the Asia Pacific Association of Hydrology and Water Resources (APHW 2004); 2004 Jun 5-9; Singapore. Hanoi: Labor and Social Publisher; 2004. p. 5-8.
Duda RO, Hart PE, Stork DG. Pattern classification. 2nd ed. New York: Wiley-Interscience; 2000.
Flores WG, Pereira WCA, Infantosi AFC. Improving classification performance of breast lesions on ultrasonography. Pattern Recognition. 2015; 48(4):1125-36. http://dx.doi.org/10.1016/j.patcog.2014.06.006.
Flores WG. Desarrollo de una metodología computacional para la clasificación de lesiones de mama en imágenes ultrasónicas [thesis]. México: Instituto Politécnico Nacional; 2009.
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Haykin SS. Neural networks and learning machines. Upper Saddle River: Prentice Hall; 2009.
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Huang YL, Chen DR, Jiang YR, Kuo SJ, Wu HK, Moon WK. Computer-aided diagnosis using morphological features for classifying breast lesions on ultrasound. Ultrasound in Obstetrics & Gynecology. 2008; 32(4):565-72. PMid:18383556. http://dx.doi.org/10.1002/uog.5205.
Huang YL, Wang KL, Chen D-R. Diagnosis of breast tumors with ultrasonic texture analysis using support vector machines. Neural Computing & Applications. 2006; 15(2):164-9. http://dx.doi.org/10.1007/s00521-005-0019-5.
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Kohavi R. A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proceedings of the 14th International Joint Conference on Artificial Intelligence; 1995. Montreal, Canada. San Francisco: Morgan Kaufmann Publishers; 1995. p. 1137-43.
Kramer D, Aghdasi F. Texture analysis techniques for the classification of microcalcifications in digitised mammograms. In: Proceedings of the IEEE AFRICON’99 Conference; 1999 Sept 28-Oct 1; Cape Town, South Africa. New York: IEEE; 1999. v. 1, p. 395-400.
Kuo W-J, Chang R-F, Chen D-R, Lee C. Data mining with decision trees for diagnosis of breast tumor in medical ultrasonic images. Breast Cancer Research and Treatment. 2001; 66(1):51-7. PMid:11368410. http://dx.doi.org/10.1023/A:1010676701382.
Leisch F, Jain LC, Hornik K. Cross-validation with active pattern selection for neural-network classifiers. IEEE Transactions on Neural Networks. 1998; 9(1):35-41. PMid:18252427. http://dx.doi.org/10.1109/72.655027.
Moré J. The levenberg-marquardt algorithm: implementation and theory. In: Watson GA, editor. Numerical analysis. Heidelberg: Springer; 1978. p. 105-16. Lecture Notes in Mathematics, 630.
Piliouras N, Kalatzis I, Dimitropoulos N, Cavouras D. Development of the cubic least squares mapping linear-kernel support vector machine classifier for improving the characterization of breast lesions on ultrasound. Computerized Medical Imaging and Graphics. 2004; 28(5):247-55. PMid:15249070. http://dx.doi.org/10.1016/j.compmedimag.2004.04.003.
Renjie L, Tao W, Zengchang Q. Classification of benign and malignant breast tumors in ultrasound images based on multiple sonographic and textural features. In: International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC 2011); 2011 Aug 26-27; Hangzhou, China. Los Alamitos: IEEE Computer Society; 2011. p. 71-4.
Shen WC, Chang RF, Moon WK, Chou YH, Huang CS. Breast ultrasound computer-aided diagnosis using bi-rads features. Academic Radiology. 2007; 14(8):928-39. PMid:17659238. http://dx.doi.org/10.1016/j.acra.2007.04.016.
Skaane P. Ultrasonography as adjunct to mammography in the evaluation of breast tumors. Acta Radiologica. Supplementum. 1999; 420:1-47. PMid:10693544.
Sociedade Brasileira de Cancerologia. [internet]. Salvador: Sociedade Brasileira de Cancerologia; 2015 [cited 2015 Oct 02] Available from: http://www.sbcancer.org.br/home2/site/index.php?option=com_content&view=article&id=110:cancer-de-mama&catid=29&Itemid=123
Theodoridis S, Koutroumbas K. Pattern recognition. 4th ed. Cambridge: Academic Press; 2008.
Uniyal N, Eskandari H, Abolmaesumi P, Sojoudi S, Gordon P, Warren L, Rohling RN, Salcudean SE, Moradi M. A new approach to ultrasonic detection of malignant breast tumors. In: IEEE International Ultrasonics Symposium (IUS); 2013 July; Prague, Czech Republic. New York: IEEE; 2013. p. 96-9.
World Health Organization – WHO. Breast cancer: prevention and control [internet]. Geneva: WHO; 2014 [cited 2015 Oct 02] Available from: http://www.who.int/cancer/detection/breastcancer/en/
Wu W-J, Moon WK. Ultrasound breast tumor image computer-aided diagnosis with texture and morphological features. Academic Radiology. 2008; 15(7):873-80. PMid:18572123. http://dx.doi.org/10.1016/j.acra.2008.01.010.
Yang MC, Moon WK, Wang YCF, Bae MS, Huang CS, Chen JH, Chang RF. Robust texture analysis using multi-resolution gray-scale invariant features for breast sonographic tumor diagnosis. IEEE Transactions on Medical Imaging. 2013; 32(12):2262-73. PMid:24001985. http://dx.doi.org/10.1109/TMI.2013.2279938.
Alvarenga AV, Infantosi AFC, Pereira WCA, Azevedo CM. Assessing the performance of morphological parameters in distinguishing breast tumors on ultrasound images. Medical Engineering & Physics. 2010; 32(1):49-56. PMid:19926514. http://dx.doi.org/10.1016/j.medengphy.2009.10.007.
Alvarenga AV, Infantosi AFC, Pereira WCA, Azevedo CM. Assessing the combined performance of texture and morphological parameters in distinguishing breast tumors in ultrasound images. Medical Physics. 2012; 39(12):7350-8. PMid:23231284. http://dx.doi.org/10.1118/1.4766268.
Alvarenga AV, Pereira WCA, Infantosi AFC, Azevedo CM. Classification of breast tumours on ultrasound images using morphometric parameters. In: Ruano MG, Ruano AE, editors. Proceedings of the International Workshop on Intelligent Signal Processing; 2005 Sept 1-3; Algarve, Portugal. Piscataway: IEEE; 2005. p. 206-10.
Chang RF, Wu W-J, Moon WK, Chen D-R. Improvement in breast tumor discrimination by support vector machines and speckle-emphasis texture analysis. Ultrasound in Medicine & Biology. 2003; 29(5):679-86. PMid:12754067. http://dx.doi.org/10.1016/S0301-5629(02)00788-3.
Chen C-M, Chou Y-H, Han K-C, Hung G-S, Tiu C-M, Chiou H-J, Chiou SY. Breast lesions on sonograms: computer-aided diagnosis with nearly setting-independent features and artificial neural networks. Radiology. 2003; 226(2):504-14. PMid:12563146. http://dx.doi.org/10.1148/radiol.2262011843.
Chen D-R, Chang R-F, Chen C-J, Ho M-F, Kuo S-J, Chen S-T, Hung S-J and Moon W-K. Classification of breast ultrasound images using fractal feature. Clinical Imaging. 2005; 29(4):235-45.
Chen D-R, Chang R-F, Huang Y-L. Computer-aided diagnosis applied to us of solid breast nodules by using neural networks. Radiology. 1999; 213(2):407-12. PMid:10551220. http://dx.doi.org/10.1148/radiology.213.2.r99nv13407.
Cheng HD, Shan J, Ju W, Guo Y, Zhang L. Automated breast cancer detection and classification using ultrasound images: a survey. Pattern Recognition. 2010; 43(1):299-317. http://dx.doi.org/10.1016/j.patcog.2009.05.012.
Chiang HK, Tiu C-M, Hung G-S, Wu S-C, Chang TY, Chou Y-H. Stepwise logistic regression analysis of tumor contour features for breast ultrasound diagnosis. In: IEEE International Ultrasonics Symposium (IUS); 2001; Atlanta, USA. New York: IEEE; 2001. v. 2, p. 1303-6.
Dennis MA, Parker SH, Klaus AJ, Stavros AT, Kaske TI, Clark SB. Breast biopsy avoidance: The value of normal mammograms and normal sonograms in the setting of a palpable lump. Radiology. 2001; 219(1):186-91. PMid:11274555. http://dx.doi.org/10.1148/radiology.219.1.r01ap35186.
Doan CD, Liong S-Y, editors. Generalization for multilayer neural network: Bayesian regularization or early stopping. In: Proceedings of the 2nd Conference of the Asia Pacific Association of Hydrology and Water Resources (APHW 2004); 2004 Jun 5-9; Singapore. Hanoi: Labor and Social Publisher; 2004. p. 5-8.
Duda RO, Hart PE, Stork DG. Pattern classification. 2nd ed. New York: Wiley-Interscience; 2000.
Flores WG, Pereira WCA, Infantosi AFC. Improving classification performance of breast lesions on ultrasonography. Pattern Recognition. 2015; 48(4):1125-36. http://dx.doi.org/10.1016/j.patcog.2014.06.006.
Flores WG. Desarrollo de una metodología computacional para la clasificación de lesiones de mama en imágenes ultrasónicas [thesis]. México: Instituto Politécnico Nacional; 2009.
Hagan MT, Demuth HB, Beale MH, De Jesus O. Neural network design [internet] 2nd ed. 2016 [cited 2013 Dec 09]. Available from: http://www.mathworks.com/products/neural-network/description6.html
Hanley JA, McNeil BJ. The meaning and use of the area under a Receiver Operating Characteristic (ROC) curve. Radiology. 1982; 143(1):29-36. PMid:7063747. http://dx.doi.org/10.1148/radiology.143.1.7063747.
Haykin SS. Neural networks and learning machines. Upper Saddle River: Prentice Hall; 2009.
Horsch K, Giger ML, Venta LA, Vyborny CJ. Computerized diagnosis of breast lesions on ultrasound. Medical Physics. 2002; 29(2):157-64. PMid:11865987. http://dx.doi.org/10.1118/1.1429239.
Huang YL, Chen DR, Jiang YR, Kuo SJ, Wu HK, Moon WK. Computer-aided diagnosis using morphological features for classifying breast lesions on ultrasound. Ultrasound in Obstetrics & Gynecology. 2008; 32(4):565-72. PMid:18383556. http://dx.doi.org/10.1002/uog.5205.
Huang YL, Wang KL, Chen D-R. Diagnosis of breast tumors with ultrasonic texture analysis using support vector machines. Neural Computing & Applications. 2006; 15(2):164-9. http://dx.doi.org/10.1007/s00521-005-0019-5.
Instituto Nacional do Câncer – INCA. Ministério da Saúde. [internet]. Rio de Janeiro: INCA; 2015 [cited 2015 Oct 02] Available from: http://www2.inca.gov.br/wps/wcm/connect/tiposdecancer/site/home/mama/deteccao_precoce
Kohavi R. A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proceedings of the 14th International Joint Conference on Artificial Intelligence; 1995. Montreal, Canada. San Francisco: Morgan Kaufmann Publishers; 1995. p. 1137-43.
Kramer D, Aghdasi F. Texture analysis techniques for the classification of microcalcifications in digitised mammograms. In: Proceedings of the IEEE AFRICON’99 Conference; 1999 Sept 28-Oct 1; Cape Town, South Africa. New York: IEEE; 1999. v. 1, p. 395-400.
Kuo W-J, Chang R-F, Chen D-R, Lee C. Data mining with decision trees for diagnosis of breast tumor in medical ultrasonic images. Breast Cancer Research and Treatment. 2001; 66(1):51-7. PMid:11368410. http://dx.doi.org/10.1023/A:1010676701382.
Leisch F, Jain LC, Hornik K. Cross-validation with active pattern selection for neural-network classifiers. IEEE Transactions on Neural Networks. 1998; 9(1):35-41. PMid:18252427. http://dx.doi.org/10.1109/72.655027.
Moré J. The levenberg-marquardt algorithm: implementation and theory. In: Watson GA, editor. Numerical analysis. Heidelberg: Springer; 1978. p. 105-16. Lecture Notes in Mathematics, 630.
Piliouras N, Kalatzis I, Dimitropoulos N, Cavouras D. Development of the cubic least squares mapping linear-kernel support vector machine classifier for improving the characterization of breast lesions on ultrasound. Computerized Medical Imaging and Graphics. 2004; 28(5):247-55. PMid:15249070. http://dx.doi.org/10.1016/j.compmedimag.2004.04.003.
Renjie L, Tao W, Zengchang Q. Classification of benign and malignant breast tumors in ultrasound images based on multiple sonographic and textural features. In: International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC 2011); 2011 Aug 26-27; Hangzhou, China. Los Alamitos: IEEE Computer Society; 2011. p. 71-4.
Shen WC, Chang RF, Moon WK, Chou YH, Huang CS. Breast ultrasound computer-aided diagnosis using bi-rads features. Academic Radiology. 2007; 14(8):928-39. PMid:17659238. http://dx.doi.org/10.1016/j.acra.2007.04.016.
Skaane P. Ultrasonography as adjunct to mammography in the evaluation of breast tumors. Acta Radiologica. Supplementum. 1999; 420:1-47. PMid:10693544.
Sociedade Brasileira de Cancerologia. [internet]. Salvador: Sociedade Brasileira de Cancerologia; 2015 [cited 2015 Oct 02] Available from: http://www.sbcancer.org.br/home2/site/index.php?option=com_content&view=article&id=110:cancer-de-mama&catid=29&Itemid=123
Theodoridis S, Koutroumbas K. Pattern recognition. 4th ed. Cambridge: Academic Press; 2008.
Uniyal N, Eskandari H, Abolmaesumi P, Sojoudi S, Gordon P, Warren L, Rohling RN, Salcudean SE, Moradi M. A new approach to ultrasonic detection of malignant breast tumors. In: IEEE International Ultrasonics Symposium (IUS); 2013 July; Prague, Czech Republic. New York: IEEE; 2013. p. 96-9.
World Health Organization – WHO. Breast cancer: prevention and control [internet]. Geneva: WHO; 2014 [cited 2015 Oct 02] Available from: http://www.who.int/cancer/detection/breastcancer/en/
Wu W-J, Moon WK. Ultrasound breast tumor image computer-aided diagnosis with texture and morphological features. Academic Radiology. 2008; 15(7):873-80. PMid:18572123. http://dx.doi.org/10.1016/j.acra.2008.01.010.
Yang MC, Moon WK, Wang YCF, Bae MS, Huang CS, Chen JH, Chang RF. Robust texture analysis using multi-resolution gray-scale invariant features for breast sonographic tumor diagnosis. IEEE Transactions on Medical Imaging. 2013; 32(12):2262-73. PMid:24001985. http://dx.doi.org/10.1109/TMI.2013.2279938.
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