A Novel Score Fusion Approach for Breast Cancer Diagnosis

Yıl 2019, Cilt: 7 Sayı: 3, 1045 - 1060, 31.07.2019

Erdem Yavuz , Can Eyüpoğlu

https://doi.org/10.29130/dubited.488460

Cited By: 6

Öz

Early diagnosis of breast cancer disease is critical for
patients to recover from this disease entirely as it is a common disease all
over the world. In order to facilitate the diagnosis of the disease, medical
doctors can benefit from computer-aided expert systems. In this paper, a score
fusion method based on generalized regression neural network (GRNN) and feed
forward neural network (FFNN) has been proposed so as to split breast cancer
data samples into benign or malignant classes. The proposed method is tested on
the Wisconsin Diagnostic Breast Cancer (WDBC) dataset. The utilities of these
basic neural networks and the proposed method are examined and comparative performance
results are presented. The experimental results show that the proposed method
is promising for the diagnosis of breast cancer and may be used as an assisting
tool in the decision-making of medical professionals.

Anahtar Kelimeler

Score Fusion, Breast Cancer Diagnosis, Neural Network, Feed Forward, Generalized Regression

Meme Kanseri Teşhisi İçin Yeni Bir Skor Füzyon Yaklaşımı

Öz

Meme kanseri tüm dünyada yaygın bir
hastalık olması sebebiyle hastalığın erken teşhisi, hastaların bu hastalıktan
tamamen kurtulabilmeleri açısından kritik öneme sahiptir. Hastalığın teşhisini
kolaylaştırmak için tıp doktorları bilgisayar destekli uzman sistemlerden
yararlanabilmektedir. Bu çalışmada meme kanseri veri örneklerini iyi huylu veya
kötü huylu sınıflarına ayırmak için genel regresyon sinir ağı (Generalized Regression
Neural Network-GRNN) ve ileri beslemeli sinir ağı (Feed Forward Neural Network-FFNN)
temelli bir skor füzyon yöntemi önerilmiştir. Önerilen yöntem Wisconsin Teşhis
Meme Kanseri (Wisconsin Diagnostic Breast Cancer-WDBC) veri seti üzerinde test
edilmiştir. Bu iki temel ağın ve önerilen yöntemin kullanışlılığı incelenmiş ve
performans sonuçları karşılaştırmalı olarak sunulmuştur. Önerilen yöntem
sınıflandırma doğruluğu bakımından literatürde WDBC veri setini kullanarak
yapılan mevcut çalışmalar ile kıyaslanmıştır. Elde edilen deneysel sonuçlar
önerilen yöntemin, meme kanseri teşhisi için umut vadettiğini ve tıp
uzmanlarının hastalığa ilişkin karar vermelerinde yardımcı bir araç olarak
kullanılabileceğini göstermektedir. 

Anahtar Kelimeler

Skor Füzyon, Meme Kanseri Teşhisi, Sinir Ağı, İleri Besleme, Genel Regresyon

Kaynakça

• [1] Ł. Jeleń, A. Krzyżak, T. Fevens and M. Jeleń, “Influence of feature set reduction on breast cancer malignancy classification of fine needle aspiration biopsies,” Computers in Biology and Medicine, vol. 79, pp. 80-91, 2016.
• [2] S. Mittal, H. Kaur, N. Gautam and A.K. Mantha, “Biosensors for breast cancer diagnosis: A review of bioreceptors, biotransducers and signal amplification strategies,” Biosensors and Bioelectronics, vol. 88, pp. 217-231, 2017.
• [3] C. DeSantis, J. Ma, L. Bryan and A. Jemal, “Breast cancer statistics, 2013,” CA: A Cancer Journal for Clinicians, vol. 64, no. 1, pp. 52-62, 2014.
• [4] D.E. Misek and E.H. Kim, “Protein Biomarkers for the Early Detection of Breast Cancer,” International Journal of Proteomics, vol. 2011, pp. 1-9, 2011.
• [5] Y. Tang, Y. Wang, M.F. Kiani and B. Wang, “Classification, Treatment Strategy, and Associated Drug Resistance in Breast Cancer,” Clinical Breast Cancer, vol. 16, no. 5, pp. 335-343, 2016.
• [6] M. Nilashi, O. Ibrahim, H. Ahmadi and L. Shahmoradi, “A knowledge-based system for breast cancer classification using fuzzy logic method,” Telematics and Informatics, vol. 34, no. 4, pp. 133-144, 2017.
• [7] L.E.M. Duijm, J.H. Groenewoud, F.H. Jansen, J. Fracheboud, M. van Beek and H.J. de Koning, “Mammography screening in the Netherlands: delay in the diagnosis of breast cancer after breast cancer screening,” British Journal of Cancer, vol. 91, no. 10, pp. 1795-1799, 2004.
• [8] A.M. Abdel-Zaher and A.M. Eldeib, “Breast cancer classification using deep belief networks,” Expert Systems With Applications, vol. 46, pp. 139-144, 2016.
• [9] G.I. Salama, M.B. Abdelhalim and M.A. Zeid, “Breast Cancer diagnosis on three different datasets using multi-classifiers,” International Journal of Computer and Information Technology, vol. 1, pp. 36-43, 2012.
• [10] Breast cancer society of Canada incidence statistics for 2015, (13 Aralık 2017). [Online]. Erişim: http://www.cbcf.org/central/AboutBreastCancerMain/FactsStats/Pages/Breast-Cancer-Canada. aspx
• [11] C. Eyupoglu, “Breast cancer classification using k-nearest neighbors algorithm,” The Online Journal of Science and Technology, vol. 8, no. 3, pp. 29-34, 2018.
• [12] M.H.C. Law, M.A.T. Figueiredo and A.K. Jain, “Simultaneous feature selection and clustering using mixture models,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 9, pp. 1154-1166, 2004.
• [13] P. Luukka and T. Leppälampi, “Similarity classifier with generalized mean applied to medical data,” Computers in Biology and Medicine, vol. 36, no. 9, pp. 1026-1040, 2006.
• [14] D.-C. Li and C.-W. Liu, “A class possibility based kernel to increase classification accuracy for small data sets using support vector machines,” Expert Systems with Applications, vol. 37, no. 4, pp. 3104-3110, 2010.
• [15] X. Liu and Y. Ren, “Novel artificial intelligent techniques via AFS theory: Feature selection, concept categorization and characteristic description,” Applied Soft Computing, vol. 10, no. 3, pp. 793-805, 2010.
• [16] D. Miao, C. Gao, N. Zhang and Z. Zhang, “Diverse reduct subspaces based co-training for partially labeled data,” International Journal of Approximate Reasoning, vol. 52, no. 8, pp. 1103-1117, 2011.
• [17] D. Lavanya and K.U. Rani, “Performance evaluation of decision tree classifiers on medical datasets,” International Journal of Computer Applications, vol. 26, no. 4, pp. 1-4, 2011.
• [18] S. Maldonado, R. Weber and J. Basak, “Simultaneous feature selection and classification using kernel-penalized support vector machines,” Information Sciences, vol. 181, no. 1, pp. 115-128, 2011.
• [19] D. Koloseni, J. Lampinen and P. Luukka, “Differential evolution based nearest prototype classifier with optimized distance measures for the features in the data sets,” Expert Systems with Applications, vol. 40, no. 10, pp. 4075-4082, 2013.
• [20] C.A. Astudillo and B.J. Oommen, “On achieving semi-supervised pattern recognition by utilizing tree-based SOMs,” Pattern Recognition, vol. 46, no. 1, pp. 293-304, 2013.
• [21] S. Tabakhi, P. Moradi and F. Akhlaghian, “An unsupervised feature selection algorithm based on ant colony optimization,” Engineering Applications of Artificial Intelligence, vol. 32, pp. 112-123, 2014.
• [22] C.K. Lim and C.S. Chan, “A weighted inference engine based on interval-valued fuzzy relational theory,” Expert Systems with Applications, vol. 42, no. 7, pp. 3410-3419, 2015.
• [23] H. Kong, Z. Lai, X. Wang and F. Liu, “Breast cancer discriminant feature analysis for diagnosis via jointly sparse learning,” Neurocomputing, vol. 177, pp. 198-205, 2016.
• [24] B. Xue, M. Zhang and W.N. Browne, “Particle swarm optimisation for feature selection in classification: Novel initialisation and updating mechanisms,” Applied Soft Computing, vol. 18, pp. 261-276, 2014.
• [25] M. Nilashi, O. Ibrahim, H. Ahmadi and L. Shahmoradi, “A knowledge-based system for breast cancer classification using fuzzy logic method,” Telematics and Informatics, vol. 34, no. 4, pp. 133-144, 2017.
• [26] W.H. Wolberg, W.N. Street and O.L. Mangasarian, Wisconsin Breast Cancer Database, University of Wisconsin Hospitals, Madison, Wisconsin, USA, November 1995.
• [27] W.N. Street, W.H. Wolberg and O.L. Mangasarian “Nuclear feature extraction for breast tumor diagnosis,” International Symposium on Electronic Imaging: Science and Technology, vol. 1905, pp. 861-870, San Jose, CA, 1993.
• [28] O.L. Mangasarian, W.N. Street and W.H. Wolberg, “Breast cancer diagnosis and prognosis via linear programming,” Operations Research, vol. 43, no. 4, pp. 570-577, July-August 1995.
• [29] W.H. Wolberg, W.N. Street and O.L. Mangasarian, “Machine learning techniques to diagnose breast cancer from fine-needle aspirates,” Cancer Letters, vol. 77, pp. 163-171, 1994.
• [30] W.H. Wolberg, W.N. Street and O.L. Mangasarian, “Image analysis and machine learning applied to breast cancer diagnosis and prognosis,” Analytical and Quantitative Cytology and Histology, vol. 17, no. 2, pp. 77-87, April 1995.
• [31] W.H. Wolberg, W.N. Street, D.M. Heisey and O.L. Mangasarian, “Computerized breast cancer diagnosis and prognosis from fine needle aspirates,” Archives of Surgery, vol. 130, pp. 511-516, 1995.
• [32] W.H. Wolberg, W.N. Street, D.M. Heisey and O.L. Mangasarian, “Computer-derived nuclear features distinguish malignant from benign breast cytology,” Human Pathology, vol. 26, pp. 792-796, 1995.
• [33] M. Lichman, UCI Machine Learning Repository, University of California, School of Information and Computer Science, Irvine, California, USA, 2013.
• [34] D.F. Specht, “Probabilistic neural networks,” Neural Networks, vol. 3, pp. 109-118, 1990.
• [35] D.F. Specht, “A general regression neural network,” IEEE Transactions on Neural Networks, vol. 2, no. 6, pp. 568-576, 1991.
• [36] S.A. Hannan, R.R. Manza and R.J. Ramteke, “Generalized Regression Neural Network and Radial Basis Function for Heart Disease Diagnosis,” International Journal of Computer Applications, vol. 7, no. 13, pp. 7-13, 2010.
• [37] M.M. Bauer, “General Regression Neural Network for Technical Use,” Master's Thesis, University of Wisconsin-Madison, 1995.
• [38] E. Yavuz, M.C. Kasapbaşı, C. Eyüpoğlu and R. Yazıcı, “An epileptic seizure detection system based on cepstral analysis and generalized regression neural network,” Biocybernetics and Biomedical Engineering, vol. 38, no. 2, pp. 201-216, 2018.
• [39] R.J. Schalkoff, Artificial Neural Networks, McGraw-Hill, pp. 337-341, 1997.
• [40] K.L. Du and M.N.S. Swamy, Neural Networks in a Softcomputing Framework, Springer Science & Business Media, pp. 251-254, 2006.
• [41] E. Yavuz, C. Eyupoglu, U. Sanver and R. Yazici, “An ensemble of neural networks for breast cancer diagnosis,” IEEE International Conference on Computer Science and Engineering (UBMK), Antalya, Turkey, 5-8 October 2017, pp. 538-543.