Abstract—Brain cancer is one of the most dangerous cancers that can attack anyone, so early detection needs to be done so that brain cancer can be treated quickly. The purpose of this study is to develop a new procedure of modeling radial basis function neural network (RBFNN) using singular value decomposition (SVD) method and to apply the procedure to diagnose brain cancer. This study uses 114 brain Magnetic Resonance Images (MRI). The RBFNN model is constructed by using steps as follows; image preprocessing, image extracting using Gray Level Co-Occurrence Matrix (GLCM), determining of parameters of radial basis function and determining of weights of neural network. The input variables are 14 features of image extractions and the output variable is a classification of brain cancer. Before learning process, the input data is normalized. The modeling is done by using K-means clustering method where the activation function in the hidden layer is Gaussian function and by determining the optimum weights of the model using SVD method. The best RBFNN model is 14 input neurons, 10 hidden layer neurons, and 1 output neuron. The results show that the sensitivity, specificity, and accuracy of RBFNN diagnoses with backpropagation equal to those of RBFNN with SVD. However, the RBFNN-SVD delivers an advantage in the running speed of the program.
Index Terms—Radial basis function neural network, diagnosis brain cancer, singular value decomposition.
Agus Maman Abadi and Dhoriva Urwatul Wutsqa are with Mathematics Departnment, Yogyakarta State University, Karangmalang Yogyakarta Indonesia, 55281 (e-mail: email@example.com, firstname.lastname@example.org). Nurhayadi is with Mathematics Education Department, Tadulako University, Sulawesi Tengah, Indonesia (e-mail: email@example.com).
Cite: Agus Maman Abadi, Dhoriva Urwatul Wustqa, and Nurhayadi, "Diagnosis of Brain Cancer Using Radial Basis Function Neural Network with Singular Value Decomposition Method," International Journal of Machine Learning and Computing vol. 9, no. 4, pp. 527-532, 2019.Copyright © 2019 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).