Cancer Detection Using Aritifical Neural Network and Support Vector Machine: A Comparative Study
DOI:
https://doi.org/10.11113/jt.v65.1788Keywords:
Support vector machine, artificial neural network, classification, cancer, accuracyAbstract
Accurate diagnosis of cancer plays an importance role in order to save human life. The results of the diagnosis indicate by the medical experts are mostly differentiated based on the experience of different medical experts. This problem could risk the life of the cancer patients. From the literature, it has been found that Artificial Intelligence (AI) machine learning classifiers such as an Artificial Neural Network (ANN) and Support Vector Machine (SVM) can help doctors in diagnosing cancer more precisely. Both of them have been proven to produce good performance of cancer classification accuracy. The aim of this study is to compare the performance of the ANN and SVM classifiers on four different cancer datasets. For breast cancer and liver cancer dataset, the features of the data are based on the condition of the organs which is also called as standard data while for prostate cancer and ovarian cancer; both of these datasets are in the form of gene expression data. The datasets including benign and malignant tumours is specified to classify with proposed methods. The performance of both classifiers is evaluated using four different measuring tools which are accuracy, sensitivity, specificity and Area under Curve (AUC). This research has shown that the SVM classifier can obtain good performance in classifying cancer data compare to ANN classifier.
References
Sattlecker M. 2011. Optimisation of Machine Learning Methods for Cancer Diagnostics using Vibrational Spectroscopy. PhD Thesis. Cranfield University, United Kingdom.
Chu F., W. Xie, and L. Wang. 2004. Gene Selection and Cancer Classification using A Fuzzy Neural Network. IEEE.
Polat K., S. Sahan, H. Kodaz, and S. Gunes. 2007. Breast Cancer and Liver Disorders Classification using Artificial Immune Recognition System (AIRS) with Performance Evaluation by Fuzzy Resource Allocation mechanism. Expert System with Applications. 32: 172–183.
Saravanan V., and R. Mallika. 2009. An Effective Classification Model For Cancer Diagnosis using Micro Array Gene Expression Data. 38th International Conference on Computer Engineering & Technology. 1: 137–141.
Keyvanfard F., M. A. Shoorehdeli, and M. Teshnehlab. 2011. Feature Selection and Classification of Breast Cancer on Dynamic Magnetic Resonance Imaging using ANN and SVM. American Journal of Biomedical Engineering. 1: 20–25.
Ren J. 2012. ANN vs. SVM: Which One Performs Better in Classification of MCCs in Mammogram Imaging. Knowledge-Based Systems. 26: 144–153.
Subashini T. S., V. Ramalingam, and S. Palanivel. 2009. Breast Mass Classification Based on Cytological Patterns using RBFNN and SVM. Expert Systems with Applications. 36: 5284–5290.
Pan S. M., and C. H. Lin. 2010. Fractal Features Classification for Liver Biopsy Images Using Neural Network-Based Classifier, International Symposium on Computer, Communication, Control and Automation. 2: 227–230.
Azmi M. S., and Z. C. Cob. 2010. Breast Cancer Prediction Based on Backpropagation Algorithm. Student Conference on Research and Development (SCOReD). 164–168.
Wu Y., N. Wang, H. Zhang, L. Qin, Z. Yan, and Y. Wu. 2010. Application of Artificial Neural Networks in the Diagnosis of Lung Cancer by Computed Tomography. Sixth International Conference on Natural Computation (ICNC). 1: 147–153.
Chu F., and L. Wang. 2006. Applying RBF Neural Networks to Cancer Classification Based on Gene Expressions, International Joint Conference on Neural Network. 1930–1934.
Parfait S., P. M. Walkera, G. Créhangea, X. Tizond, and J. Mitérana. 2011. Classification of Prostate Magnetic Resonance Spectra using Support Vector Machine. Biomedical Signal Processing and Control. 7: 499–508.
Liao R., T. Wan, and Z. Qin. 2011. Classification of Benign and Malignant Breast Tumors in Ultrasound Images Based on Multiple Sonographic and Textural Features. Third International Conference on Intelligent Human-Machine Systems and Cybernetic. 1: 71–74.
Chen A. H., and C. H. Lin. 2011. A Novel Support Vector Sampling Technique to Improve Classification Accuracy and To Identify Key Genes of Leukemia and Prostate Cancers. Expert Systems with Applications. 38: 3209–3219.
Polat K., and S. Güneş. 2007. Breast Cancer Diagnosis using Least Square Support Vector Machine. Digital Signal Processing. 17(4): 694–701.
Murat C., E. Mehmet, Z. B. Erkan, and Y. A. Ziya. 2009. Early Prostate Cancer Diagnosis by using Artificial Neural Networks and Support Vector Machines. Expert Systems with Applications. 36: 6357–6361.
Mumtaz K. 2009. Evaluation of Three Neural Network Models using Wisconsin Breast Cancer Database. International Conference on Control, Automation, Communication and Energy Conservation. 1–7.
Vapnik, V. 1995. The Nature of Statistical Learning Theory. New York: Springer.
Vapnik, V. 1998. Statistical Learning Theory. New York: Wiley.
Liu Y., and Y. F. Zheng. 2004. FS_SFS: A Novel Feature Selection Method for Support Vector Machines. IEEE International Conference on Acoustic, Speech, and Signal Processing. 5: 797–800.
Chen H. L. 2011. A Support Vector Machine Classifier with Rough Set-Based Feature Selection, Expert System Appl. 38(7): 9014–9022.
Bertsekas D. P. 1995. Nonlinear Programming. Belmont: Athena Scientific.
] Akay M. F. 2009. Support Vector Machines Combined with Feature Selection for Breast Cancer Diagnosis, Expert System Appl. 36: 3240–3247.
Chang C. C., and C. J. Lin C. J. LIBSVM: A Library for Support Vector Machine. S oftware available at http://www.csie.ntu.edu.tw/~cjlin/libsvm.
Hsu H. H., C. W. Hsieh, and M-D. Lu. 2011. Hybrid Feature Selection By Combining Filters and Wrappers. Expert System Appl. 38(7): 8144–8150.
Downloads
Published
Issue
Section
License
Copyright of articles that appear in Jurnal Teknologi belongs exclusively to Penerbit Universiti Teknologi Malaysia (Penerbit UTM Press). This copyright covers the rights to reproduce the article, including reprints, electronic reproductions, or any other reproductions of similar nature.