Research Article
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Classification of Breast Cancer using Artificial Neural Network Algorithms

Year 2021, Volume: 1 Issue: 1, 57 - 68, 30.04.2021

Abstract

Breast cancer is a malignant tumor that has developed from cells of the breast. Breast cancer is one of the most fatal diseases in the world and a relatively common cancer in Turkey. Breast cancer diagnosis has been approached by various machine learning techniques for many years. In this study, two different probabilistic neural network (PNN) structures were used for breast cancer’s diagnosis. The PNN results were compared with the results of the multilayer, learning vector quantization neural networks and the results of the previous reported studies focusing on breast cancer’s diagnosis and using the same dataset. It was observed that the PNN is the best classification accuracy with 98.10% accuracy obtained via 3-fold cross validation. The present paper describes how this technique can be applied to the breast tissue classification and the breast cancer detection for medical devices. The purpose of this study is the classification of the variability of impedivity observed in normal and pathological breast tissue.

References

  • 1. Bothorel S, Meunier BB, Muller SA, Fuzzy logic based approach for semi logical analysis of micro calcification in mammographic images, International Journal of Intelligent System 12:819, 1997.
  • 2. Silva JE, Marques JP, Jossinet de SJ, Classification of Breast Tissue by Electrical Impedance Spectroscopy, Med & Bio Eng & Computing 38:26, 2000.
  • 3. Shukla A, Tiwari R, Kaur P, Knowledge Based Approach for Diagnosis of Breast Cancer, IEEE International Advance Computing Conference, Patiala, India, pp. 6-12, 2009.
  • 4. Kadoz H, Ozsen S, Arslan A, Gunes S, Medical application of information gain based artificial immune recognition system: Diagnosis of thyroid disease, Expert System with Applications 36:3086, 2009.
  • 5. Specht DF, Probabilistic neural networks, Neural Networks 3:109, 1990.
  • 6. Er O, Yumusak N, Temurtas F, Chest diseases diagnosis using artificial neural networks, Expert Systems with Applications 37: 7648, 2010.
  • 7. Er O, Sertkaya C, Temurtas F, Tanrikulu AC, A Comparative Study on Chronic Obstructive Pulmonary and Pneumonia Diseases Diagnosis using Neural Networks and Artificial Immune System, Journal of Medical Systems 33:485, 2009.
  • 8. Er O, Yumusak N, Temurtas F, Diagnosis of chest diseases using artificial immune system, Expert Systems with Applications 39:1862, 2012.
  • 9. Er O, Tanrikulu AC, Abakay A, Temurtas F, An approach based on probabilistic neural network for diagnosis of Mesothelioma’s disease, Computers & Electrical Engineering 38:75, 2012.
  • 10. Temurtas F, A comparative study on thyroid disease diagnosis using neural networks, Expert Systems with Applications 36:944, 2009.
  • 11. Kayaer K, Yıldırım T, Medical Diagnosis on Pima Indian Diabetes Using General Regression Neural Networks, In Proc. of International Conference on Artificial Neural Networks and Neural Information Processing (ICANN/ICONIP), Istanbul, Turkey, pp. 181-184, 2003.
  • 12. Delen D, Walker G, Kadam A, Predicting breast cancer survivability: A comparison of three data mining methods, Artificial Intelligence in Medicine 34:113, 2005.
  • 13. Rumelhart DE, Hinton GE, Williams RJ, Learning internal representations by error propagation, in D.E. Rumelhart, J.L. McClelland (Eds.), Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Cambridge, England, 1:318, 1986.
  • 14. Gori M, Tesi A, On the problem of local minima in backpropagation, IEEE Trans. Pattern Anal. Machine Intell. 14:76, 1992.
  • 15. Brent RP, Fast training algorithms for multi-layer neural nets, IEEE Trans. Neural Networks 2:346,1991.
  • 16. Hagan MT, Menhaj M, Training feed forward networks with the Marquardt algorithm, IEEE Trans. Neural Networks 5:989, 1994.
  • 17. Hagan MT, Demuth HB, Beale MH, Neural Network Design, PWS Publishing, Boston, MA, pp.734, 1996.
  • 18. Er O, Temurtas F, A Study on Chronic Obstructive Pulmonary Disease Diagnosis Using Multilayer Neural Networks, Journal of Medical Systems 32:429, 2008.
  • 19. Gulbag A, Temurtas F, A study on quantitative classification of binary gas mixture using neural networks and adaptive neuro fuzzy inference systems, Sensors and Actuators B 115:252, 2006.
  • 20. Kohonen T, Improved versions of learning vector quantization. In Proc. of the IEEE International Joint Conference on Neural Networks, New York, USA, pp. 545-550, 1990.
  • 21. Kohonen T, Self-Organizing Maps, Academic Press, Berlin: Springer-Verlag, 1997. 22. Gulbag A, Temurtas F, Yusubov I, Quantitative discrimination of the binary gas mixtures using a combinational structure of the probabilistic and multilayer neural networks, Sens. Actuators B: Chem. 131:196, 2007.
  • 23. Temurtas H, Yumusak N, Temurtas F, A comparative study on diabetes disease diagnosis using neural networks, Expert Systems with Applications 36:8610, 2009.
  • 24. Aruna S, Rajagopalan SP, Nandakishore LV, Knowledge Based Analysis of Various Statistical Tools in Detecting Breast Cancer, in Computer Science & Information Technology, Melbourne, Australia, pp. 37-45, 2011.
  • 25. Jossinet J, Variability of Impedivity in Normal and Pathological Breast Tissue, Med & Bio Eng & Computing 34:346, 1996.
  • 26. Jossinet J, The Impedivity of Freshly Excised Human Breast Tissue, Physiol. Meas. 19:61, 1998.
  • 27. International Database Web Site, http://archive.ics.uci.edu./ml/datasets/Breast+Tissue (last accessed: 04.08 2014).
  • 28. Wolberg WH, Mangasarian OL, Multisurface method of pattern separation for medical diagnosis applied to breast cytology, In Proceedings of the National Academy of the Sciences 87:9193, 1990.
  • 29. Jun Zhang MS, Haobo Ma Md MS, An Implementation of Guildford Cytological Grading System to diagnose Breast Cancer Using Naive Bayesian Classifier, Academic Press, MEDINFO, Amsterdam, 2004.
  • 30. Kamruzzaman SM, Monirul IM, Extraction of Symbolic Rules from Artificial Neural Networks, Proceedings of world Academy of science, Engineering and Technology 10, pp. 271-277, 2005.
  • 31. Punitha A, Sumathi CP, Santhanam T, A Combination of Genetic Algorithm and ART Neural Network for Breast Cancer Diagnosis, Asian Journal of Information Technology 6:112, 2007.
  • 32. Setiono R, Liu H, Neural-Network Feature Selector, IEEE Transactions On Neural Networks 8: 664, 1997.
  • 33. Duch W, Adamczak R, Grabczewski K, A New methodology of Extraction, Optimization and Application of Crisp and Fuzzy Logic Rules, IEEE Transactions On Neural Networks, 12:227, 2001.
  • 34. Wu Y, Giger ML, Doi K, Vyborny CJ, Schmidt RA, Metz CE, Artificial Neural Networks in Mammography: application to decision making in the diagnosis of breast cytology, Radiology, 187:81, 1993.
  • 35 Abbass HA, An evolutionary Artificial Neural Networks Approach for Breast Cancer Diagnosis, Artificial Intelligence in Medicine 25:265, 2002.
  • 36. Esugasini S, Mohd YM, Nor Ashidi MI, Nor Hayati O, Performance Comparison for MLP Networks Using Various Back Propagation Algorithms for Breast Cancer Diagnosis, Knowledge-Based Intelligent Information and Engineering Systems, Lecture Notes in Computer Science 3682:123, 2005.
  • 37. Ayer T, Alagoz O, Chhatwal J, Shavlik JW, Kahn CEJ, Burnside ES, Breast Cancer Risk Estimation with Artificial Neural Networks Revisited: Discrimination and Calibration, Cancer 116:3310, 2010.
  • 38. Wu JD, et al., An expert system for fault diagnosis in internal combustion engines using probability neural network, Expert Systems with Applications 34:2704, 2008.
  • 39. Matlab® Release Notes, Version 2020a, The MathWorks Inc.
  • 40. Bascil MS, Temurtas F, A study on hepatitis disease diagnosis using multilayer neural network with levenberg-marquardt training algorithm, Journal of Medical Systems 35:433, 2009.
  • 41. Ozyilmaz L, Yildirim T, Diagnosis of thyroid disease using artificial neural network methods, In Proceedings of ICONIP’02 9th International Conference on Neural Information Processing, Singapore, pp. 2033-2036, 2002.
  • 42. Watkins A, AIRS: A resource limited artificial immune classifier, Academic Press, Master Thesis, Mississippi State University, USA, 2001.
Year 2021, Volume: 1 Issue: 1, 57 - 68, 30.04.2021

Abstract

References

  • 1. Bothorel S, Meunier BB, Muller SA, Fuzzy logic based approach for semi logical analysis of micro calcification in mammographic images, International Journal of Intelligent System 12:819, 1997.
  • 2. Silva JE, Marques JP, Jossinet de SJ, Classification of Breast Tissue by Electrical Impedance Spectroscopy, Med & Bio Eng & Computing 38:26, 2000.
  • 3. Shukla A, Tiwari R, Kaur P, Knowledge Based Approach for Diagnosis of Breast Cancer, IEEE International Advance Computing Conference, Patiala, India, pp. 6-12, 2009.
  • 4. Kadoz H, Ozsen S, Arslan A, Gunes S, Medical application of information gain based artificial immune recognition system: Diagnosis of thyroid disease, Expert System with Applications 36:3086, 2009.
  • 5. Specht DF, Probabilistic neural networks, Neural Networks 3:109, 1990.
  • 6. Er O, Yumusak N, Temurtas F, Chest diseases diagnosis using artificial neural networks, Expert Systems with Applications 37: 7648, 2010.
  • 7. Er O, Sertkaya C, Temurtas F, Tanrikulu AC, A Comparative Study on Chronic Obstructive Pulmonary and Pneumonia Diseases Diagnosis using Neural Networks and Artificial Immune System, Journal of Medical Systems 33:485, 2009.
  • 8. Er O, Yumusak N, Temurtas F, Diagnosis of chest diseases using artificial immune system, Expert Systems with Applications 39:1862, 2012.
  • 9. Er O, Tanrikulu AC, Abakay A, Temurtas F, An approach based on probabilistic neural network for diagnosis of Mesothelioma’s disease, Computers & Electrical Engineering 38:75, 2012.
  • 10. Temurtas F, A comparative study on thyroid disease diagnosis using neural networks, Expert Systems with Applications 36:944, 2009.
  • 11. Kayaer K, Yıldırım T, Medical Diagnosis on Pima Indian Diabetes Using General Regression Neural Networks, In Proc. of International Conference on Artificial Neural Networks and Neural Information Processing (ICANN/ICONIP), Istanbul, Turkey, pp. 181-184, 2003.
  • 12. Delen D, Walker G, Kadam A, Predicting breast cancer survivability: A comparison of three data mining methods, Artificial Intelligence in Medicine 34:113, 2005.
  • 13. Rumelhart DE, Hinton GE, Williams RJ, Learning internal representations by error propagation, in D.E. Rumelhart, J.L. McClelland (Eds.), Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Cambridge, England, 1:318, 1986.
  • 14. Gori M, Tesi A, On the problem of local minima in backpropagation, IEEE Trans. Pattern Anal. Machine Intell. 14:76, 1992.
  • 15. Brent RP, Fast training algorithms for multi-layer neural nets, IEEE Trans. Neural Networks 2:346,1991.
  • 16. Hagan MT, Menhaj M, Training feed forward networks with the Marquardt algorithm, IEEE Trans. Neural Networks 5:989, 1994.
  • 17. Hagan MT, Demuth HB, Beale MH, Neural Network Design, PWS Publishing, Boston, MA, pp.734, 1996.
  • 18. Er O, Temurtas F, A Study on Chronic Obstructive Pulmonary Disease Diagnosis Using Multilayer Neural Networks, Journal of Medical Systems 32:429, 2008.
  • 19. Gulbag A, Temurtas F, A study on quantitative classification of binary gas mixture using neural networks and adaptive neuro fuzzy inference systems, Sensors and Actuators B 115:252, 2006.
  • 20. Kohonen T, Improved versions of learning vector quantization. In Proc. of the IEEE International Joint Conference on Neural Networks, New York, USA, pp. 545-550, 1990.
  • 21. Kohonen T, Self-Organizing Maps, Academic Press, Berlin: Springer-Verlag, 1997. 22. Gulbag A, Temurtas F, Yusubov I, Quantitative discrimination of the binary gas mixtures using a combinational structure of the probabilistic and multilayer neural networks, Sens. Actuators B: Chem. 131:196, 2007.
  • 23. Temurtas H, Yumusak N, Temurtas F, A comparative study on diabetes disease diagnosis using neural networks, Expert Systems with Applications 36:8610, 2009.
  • 24. Aruna S, Rajagopalan SP, Nandakishore LV, Knowledge Based Analysis of Various Statistical Tools in Detecting Breast Cancer, in Computer Science & Information Technology, Melbourne, Australia, pp. 37-45, 2011.
  • 25. Jossinet J, Variability of Impedivity in Normal and Pathological Breast Tissue, Med & Bio Eng & Computing 34:346, 1996.
  • 26. Jossinet J, The Impedivity of Freshly Excised Human Breast Tissue, Physiol. Meas. 19:61, 1998.
  • 27. International Database Web Site, http://archive.ics.uci.edu./ml/datasets/Breast+Tissue (last accessed: 04.08 2014).
  • 28. Wolberg WH, Mangasarian OL, Multisurface method of pattern separation for medical diagnosis applied to breast cytology, In Proceedings of the National Academy of the Sciences 87:9193, 1990.
  • 29. Jun Zhang MS, Haobo Ma Md MS, An Implementation of Guildford Cytological Grading System to diagnose Breast Cancer Using Naive Bayesian Classifier, Academic Press, MEDINFO, Amsterdam, 2004.
  • 30. Kamruzzaman SM, Monirul IM, Extraction of Symbolic Rules from Artificial Neural Networks, Proceedings of world Academy of science, Engineering and Technology 10, pp. 271-277, 2005.
  • 31. Punitha A, Sumathi CP, Santhanam T, A Combination of Genetic Algorithm and ART Neural Network for Breast Cancer Diagnosis, Asian Journal of Information Technology 6:112, 2007.
  • 32. Setiono R, Liu H, Neural-Network Feature Selector, IEEE Transactions On Neural Networks 8: 664, 1997.
  • 33. Duch W, Adamczak R, Grabczewski K, A New methodology of Extraction, Optimization and Application of Crisp and Fuzzy Logic Rules, IEEE Transactions On Neural Networks, 12:227, 2001.
  • 34. Wu Y, Giger ML, Doi K, Vyborny CJ, Schmidt RA, Metz CE, Artificial Neural Networks in Mammography: application to decision making in the diagnosis of breast cytology, Radiology, 187:81, 1993.
  • 35 Abbass HA, An evolutionary Artificial Neural Networks Approach for Breast Cancer Diagnosis, Artificial Intelligence in Medicine 25:265, 2002.
  • 36. Esugasini S, Mohd YM, Nor Ashidi MI, Nor Hayati O, Performance Comparison for MLP Networks Using Various Back Propagation Algorithms for Breast Cancer Diagnosis, Knowledge-Based Intelligent Information and Engineering Systems, Lecture Notes in Computer Science 3682:123, 2005.
  • 37. Ayer T, Alagoz O, Chhatwal J, Shavlik JW, Kahn CEJ, Burnside ES, Breast Cancer Risk Estimation with Artificial Neural Networks Revisited: Discrimination and Calibration, Cancer 116:3310, 2010.
  • 38. Wu JD, et al., An expert system for fault diagnosis in internal combustion engines using probability neural network, Expert Systems with Applications 34:2704, 2008.
  • 39. Matlab® Release Notes, Version 2020a, The MathWorks Inc.
  • 40. Bascil MS, Temurtas F, A study on hepatitis disease diagnosis using multilayer neural network with levenberg-marquardt training algorithm, Journal of Medical Systems 35:433, 2009.
  • 41. Ozyilmaz L, Yildirim T, Diagnosis of thyroid disease using artificial neural network methods, In Proceedings of ICONIP’02 9th International Conference on Neural Information Processing, Singapore, pp. 2033-2036, 2002.
  • 42. Watkins A, AIRS: A resource limited artificial immune classifier, Academic Press, Master Thesis, Mississippi State University, USA, 2001.
There are 41 citations in total.

Details

Primary Language English
Subjects Clinical Sciences, Engineering
Journal Section Research Articles
Authors

Emre Ölmez

Ourania Areta

Orhan Er This is me

Publication Date April 30, 2021
Published in Issue Year 2021 Volume: 1 Issue: 1

Cite

APA Ölmez, E., Areta, O., & Er, O. (2021). Classification of Breast Cancer using Artificial Neural Network Algorithms. Artificial Intelligence Theory and Applications, 1(1), 57-68.