International Journal of Machine Learning and Computing, Vol. 9, No. 4, August 2019

Diagnosis of Brain Cancer Using Radial Basis Function Neural Network with Singular Value Decomposition Method

Agus Maman Abadi, Dhoriva Urwatul Wustqa, and Nurhayadi

observation and analysis them. Then, they consult the results Abstract—Brain cancer is one of the most dangerous cancers of the analysis to the doctor. The diagnosis using MRI image that can attack anyone, so early detection needs to be done so can only categorize the brain as normal or abnormal. that brain cancer can be treated quickly. The purpose of this Regarding the important of the early diagnosis of brain study is to develop a new procedure of modeling radial basis cancer, many researchers are tempted doing research in this function neural network (RBFNN) using singular value field. Several methods have been applied to classify brain decomposition (SVD) method and to apply the procedure to diagnose brain cancer. This study uses 114 brain Magnetic cancer, such as artificial neural networks based on brain MRI Resonance Images (MRI). The RBFNN model is constructed by data [3], [4], and backpropagation network techniques using steps as follows; image preprocessing, image extracting (BPNN) and principal component analysis (PCA) [5], using Gray Level Co-Occurrence Matrix (GLCM), determining probabilistic neural network method [6], [7]. Several of parameters of radial basis function and determining of researches have proposed the combining several intelligent weights of neural network. The input variables are 14 features system methods, those are adaptive neuro-fuzzy inference of image extractions and the output variable is a classification of system (ANFIS), neural Elman network (Elman NN), brain cancer. Before learning process, the input data is nonlinier autoregressive with exogenous neural network normalized. The modeling is done by using K-means clustering (NARXNN), and feedforward NN [8] and artificial neural method where the in the hidden layer is network based on MRI image segmentation data using fuzzy Gaussian function and by determining the optimum weights of the model using SVD method. The best RBFNN model is 14 c-means and bounding Box method [9]. Studies to diagnose input neurons, 10 hidden layer neurons, and 1 output neuron. brain cancers have also been done using support vector The results show that the sensitivity, specificity, and accuracy of machine (SVM) and multi-SVM [10], adaptive neuro-fuzzy RBFNN diagnoses with backpropagation equal to those of inference system [11]. RBFNN with SVD. However, the RBFNN-SVD delivers an Recently, research on brain cancer diagnosis still become advantage in the running speed of the program. interesting area to develop the method in order to improve accuracy of the diagnosis. One of the methods that can be Index Terms—Radial basis function neural network, used as diagnosis tool is the radial basis function neural diagnosis brain cancer, singular value decomposition. network (RBFNN). The RBFNN is suitable for large training data, provide a good generalization. The RBFNN has simple network structure, since it has only one hidden layer. This I. INTRODUCTION property leads to speed up the learning process. Study related Brain cancer is a dangerous disease and can be fatal to to the diagnosis of brain cancer by applying RBFNN has been someone. In 2012, brain cancer cases in Indonesia are 1.9 per performed using CT scan data of normal and abnormal brain 100,000 population, while the brain mortality rate is 1.3 per [12]. The other study uses principle component analysis 100,000 inhabitants [1]. This fact shows the level of brain based on electroencephalograph (EEG) digital signals [13] cancer patients in Indonesia is high. Therefore, early and set those values as the inputs of RBFNN model. diagnosis is needed to prevent the death caused by brain The RBFNN model has been applied to diagnose various cancer. According to the Ministry of Health of the Republic types of diseases. The classification of breast cancer using of Indonesia [2], there are several steps in diagnosing brain improved F-score with support vector machine and RBF cancer (1) physical examination; (2) laboratory examination; network has been done [14]. The result shows that the and (3) radiological examination. Standard radiological improved F-score and support vector machines method gives examinations are CT Scan and MRI which produce the the better accuracy than improved F-score and RBF network digital images. The radiologists interpret the MRI images by method. The study of cancer prediction based on gene expression data using deep learning has been done [15]. The modified neural network has been applied to diagnosis of epilepsy based on EEG signals and the result shows that the ensemble TPunit neural networks method gives the better accuracy than neural network method for diagnosis of Manuscript received October 20, 2018; revised April 26, 2019. This work was supported in part by the Directorate of Research and Community epilepsy [16]. The RBFNN model has been applied for Service, Ministry of Research, Technology, and Higher Education of the detection of lung cancer based on radiograph images [17]. Republic of Indonesia under grant No. 01/Penelitian/ PDUPT/ In the RBFNN modeling, the weights of output target can UN.34.21/2018. Agus Maman Abadi and Dhoriva Urwatul Wutsqa are with Mathematics be determined by back propagation or global ridge regression Departnment, Yogyakarta State University, Karangmalang Yogyakarta methods. However, these methods require a long iteration to Indonesia, 55281 (e-mail: [email protected], [email protected]). get the solution, while singular value decomposition method Nurhayadi is with Mathematics Education Department, Tadulako University, Sulawesi Tengah, Indonesia (e-mail: [email protected]). does not require iteration and is more efficient for doi: 10.18178/ijmlc.2019.9.4.836 527 International Journal of Machine Learning and Computing, Vol. 9, No. 4, August 2019 determining the weights of output target. This study aims to The learning process in RBFNN includes unsupervised diagnose brain cancer based on MRI image data using the learning to ascertain the number of hidden neurons as well as combination of radial basis function neural network (RBFNN) to obtain the parameter values of the radial basis function, and singular value decomposition method. and supervised learning to obtain the weights between the hidden layer and output layer. The number of neurons in the hidden layer equals to the number of basis functions. The II. RADIAL BASIS FUNCTION NEURAL NETWORK unsupervised learning is performed using the K-means The RBFNN is one of the neural network model with a clustering method. It is commonly used in RBFNN modeling, three-layer, namely an input layer, a single hidden layer, and whose placement of the objects in each cluster based on the an output layer. The performance of RBFNN depends on the Euclidian distance. The Euclidian distance between two choice of exactly three important parameters (cluster center, points P(푥1, 푥2, . . . , 푥푝) and Q(푦1, 푦2, . . . , 푦푝) is expressed as distance and weight). Each input variable is assigned to the 2 neuron in the input layer and enters directly to the hidden 2 푑(푃, 푄) = √(푥1 − 푦1) +. . . +(푥푝 − 푦푝) layer without weight. In the hidden layer, the RBFNN performs a nonlinear transformation of the data from the The following steps are the K-means clustering algorithm input layer using the basic radial function before it is linearly [19]. processed at the output layer. The architecture of the RBFNN 1) Partition the data into K cluster is shown in Fig. 1. 2) Place any objects to the nearest cluster. 3) Recalculate the center of the clusters that receive 휑1 new data and the clusters that lose data.

푥1 4) Repeat step (1) to (3) until the new centers equal to 푤 the old ones. 휑 1 2 Futhermore, it will be determined the weight 휔푗 in (2) 푤2 푥2 minimized the equation (3). 푤푗 휑 푓(푥) 푗 푛 2 ⁝ 푆푆퐸 = ∑푘=1(푦푘 − 푦̂푘) (3) ⁝ 푤푘 Output Layer where n is the number of training data,⁡푦̂푘 is the output of the 푥푝 휑 푤 푡ℎ 푞 0 model for 푘 ⁡ observation, ⁡푦푘 is the target for 푡ℎ Input 푘 ⁡observation. Then, we apply (2) and (3) for all training data to get (4). layer 1 Bias

Hidden layer 푦1 푦 Fig. 1. The architecture of RBFNN. 풚 = [ 2] = 흋풘̂ (4) ⋮ 푦 In Fig. 1, (푥푖, 푖 = 1, 2, . . . 푝) are input neurons, (휑푗, 푗 = 푛 where 1, 2, . . . , 푞) are hidden neurons, and 푓(푥) is output neuron.

The weights between the hidden layer and the output layer 휑 [휇(푥)] 휑 [휇(푥)] … 휑푞[휇(푥)]1 1 1 1 2 1 are denoted as 푤푗 and the bias 푤0 is added to the hidden … 휑1[휇(푥)]2 휑2[휇(푥)]2 휑푞[휇(푥)]2 1 layer. The activation function 휑 (푥) is determined by the 흋 = ⋱⁡ (5) 푗 ⋮ ⋮ ⁡⁡ ⁡⁡⁡⁡⁡⁡⁡⋮ ⁡⁡⁡⁡⁡⁡⁡⁡1 … mean cluster and width cluster. The RBFNN model is [휑1[휇(푥)]푛 휑2[휇(푥)]푛 휑푞[휇(푥)]푛 1] characterized by the activation function, which are included in the class of radial basis function. There are several and 풘̂ is a vector of weights 푤̂푗. activation functions in RBFNN namely Gaussian function, multi quadratic inverse and Cauchy functions [18]. In this research, we use Gaussian activation function III. SINGULAR VALUE DECOMPOSITION In this section, we will introduce singular value 2 (푥−푐) decomposition of matrix and its properties referred from 휑(푥) = exp (⁡− 2 ) (1) 푟 Scheick [20]. Any m x n matrix A can be expressed as where c is the mean of cluster, r is maximum distance of each A= USV T (6) object to the mean of cluster. The output y of the RBFNN model is the linear combination of the weights 푤푗 included where U and V are orthogonal matrices of dimensions m x m, w0 and the activation function 휑푗(푥) n x n respectively and S is m x n matrix whose entries are 0 푞 except sii =  i ir= 1,2,..., with 12  ...   r  0 , r min( m , n ) . Equation (6) is called a singular value 푓(풙) = ∑ 푤푗휑푗(풙) + 푤0 푗=1 decomposition (SVD) of A and the numbers are called 2 푞 푝 (푥푖−푐푗푖) singular values of matrix A. If U i , Vi are columns of U and = ∑푗=1 푤푗exp (⁡− ∑푖=1 2 ) + 푤0 (2) 푟푗푖 V , respectively, then we can conclude as follows.

528 International Journal of Machine Learning and Computing, Vol. 9, No. 4, August 2019

∑0 T 1. AUV= with  = diag( ,  ,...,  ) . 푇푁  12 r Specificity = (10) 00 푇푁+퐹푃

2. The matrices AAT and AAT are symmetric matrices The accuracy is the ability to identify both positive and and the coloumns of U and V are eigenvectors of negative results appropriately, and it can be written as and , respectively. 2 푇푁+푇푃 3. The values i , i = 1, 2, ..., r, are nonzero eigenvalues of Accuracy = (11) 푇푁+푇푃+퐹푁+퐹푃

corresponding to eigenvectors Vi and VVrn+1,..., are eigenvectors corresponding to zero eigenvalue. 4. The values , i = 1, 2, ..., r, are nonzero eigenvalues of V. FEATURE EXTRACTION Texture is an important characteristic for identifying an corresponding to eigenvectors U and UU,..., i rm+1 object of image. Textural feature on spatial gray image is are eigenvectors corresponding to zero eigenvalue. commonly used in classifying images. Gray Level 5. AVUi=  i i for i = 1, 2,..., r and AVi = 0 for ir . Co-occurrence Matrix (GLCM) is one of extraction methods T T 6. AUVi=  i i for i = 1, 2,..., r and AUi = 0 for . to to get textural feature of the image. It has capability to give detail information of image pattern [22]. The GLCM is Furthermore, the decomposition matrix A in (6) can be included in statistical second order extracting method. written as GLCM is a matrix that stores the frequency value of the brightness differences between one pixel and other r T neigborhod pixel. AUV=  i i i (7) i=1 The use of GLCM is first introduced by [23] to extract some texture features. Several researches have used the The SVD of matrix can be applied to solve the linear GLCM method to extract the images [24]-[26]. The system Ax= d . If A is n x n invertible matrix, then r = n and parameters resulted from GLCM extraction process in this n research are 14 features involving energy, contrast, −−11 all  i  0 . Hence x= A d = i  d, Ui  V i is the i=1 correlation, sum of squares, inverse difference moment, sum solution of the linear system where , is standard inner average, sum variance, sum entropy, entropy, difference variance, difference entropy, maximum probability, product in R n . If A is singular matrix of arbitrary dimension, homogeneity, and dissimilarity [22], [23], [27], [28]. The then the solution of is mathematical formula of those features are as follows :

푁푔 푁푔 r 2 +−1 1. Energy = ∑푖=1 ∑푗=1(푖푗)푃푖푗 x= i  d, Ui  V i . (8) i=1 푁푔 푁푔 2 2. Contrast = ∑푖=1 ∑푗=1(푖 − 푗) 푃푖푗 n + Furthermore min Ax− d : x  F = Ax − d . It 푁푔 푁푔 ∑ ∑ (푖푗)푃푖푗−휇푥휇푦 3. Correlation = 푖=1 푗=1 means that the optimal solution for the linear system 휎푥휎푦 is matrix vector (8). 푁 푁 4. sum of squares = ∑ 푔 ∑ 푔 ( )2 푖=1 푗=1 푖 − 휇 푃(푖,푗) 푁 푁 푃 5. Inverse Difference Moment = ∑ 푔 ∑ 푔 푖푗 IV. DIAGNOSTIC ACCURACY 푖=1 푗=1 1+(푖−푗)2 The diagnostic accuracy can be measured by sensitivity, 2푁푔 6. sum average = ∑ 푘푃 (푘) specificity and accuracy. There are several commonly used 푘=2 푥+푦 terms along with descriptions of sensitivity, specificity and 2푁푔 2 7. sum variance = ∑ (푖 − 푆퐸) 푃푥+푦(푘) accuracy. Those are TP = True Positive, FP = False Positive, 푖=2 2푁푔 TN = True Negative, and FN = False Negative. The TP is the 8. sum entropy = − ∑푘=2 푃푥+푦(푘)푙표푔{푃푥+푦(푘)} number of patients with the disease who is diagnosed 푁 푁 9. Entropy ∑ 푔 ∑ 푔 appropriately, the FN is the the number of patients with the = − 푖=1 푙=1 푃푖푗 log⁡(푃푖푗) disease who is diagnosed inappropriately. Similiarly, the TN 10. Difference variance (퐷푉) = variansi⁡of⁡푃푥+푦(푘) is the number of patients without the disease who is 푁푔−1 diagnosed appropriately, the FP is the the number of patients 11. difference entropy =− ∑푗=0 푃푥+푦(푘)푙표푔{푃푥+푦(푘)} without the disease who is diagnosed inappropriately. The 12. maximum probability =⁡푚푎푥푖,푗{푃(푖, 푗)} sensitivity refers to the ability of the test to identify patients 푁 푁 푃 with the disease appropriately [21], and it can be written as 13. Homogenity =∑ 푔 ∑ 푔 푖푗 푖=1 푗=1 1+|푖−푗|

푇푃 푁푔−1 푁푔−1 Sensitivity = (9) 14. Dissimilarity = ∑ ∑ 푃(푖, 푗)|푖 − 푗| 푇푃+퐹푁 푖=0 푗=0

where i, j are the spatial coordinate of probability function The specificity refers to the ability of the test to identify 푃 , that is an entry ith row and jth colomn on GLCM. 푁 is patients without disease appropriately, and it can be written 푖푗 𝑔 the number of gray level of the image, and as

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푁푔 푁푔 training data, 푼⁡is a unitary matrix Σ is a singular value 휇푥 = ∑ ∑ 푖푃(푖, 푗) 푖=1 푗=1 matrix, 푽푇 =unitary matrix 푁 푁 휇 = ∑ 푔 ∑ 푔 푗푃(푖, 푗) 휎 0 0 0 ⋯ 0 푦 푖=1 푗=1 1 0 휎2 0 0 ⋯ 0 푁 푁 푔 푔 2 Σ = 0 0 휎3 ⋯ 0 (13) 휎푥 = ∑ ∑ (푖 − 휇푥) 푃(푖, 푗) ⋮ 푖=1 푗=1 ⋮ ⋮ ⋮ ⋱ ⋮ 0 푁푔 푁푔 2 [ 0 0 0 휎푛 0]푛×(푞+1) 휎푦 = ∑푖=1 ∑푗=1(푗 − 휇푦) 푃(푖, 푗) and 푁푔 푁푔 푃푥+푦(푘) = ∑푖=1 ∑푗=1 푃(푖, 푗); ⁡⁡⁡푖 + 푗 = 푘;⁡⁡⁡⁡ 휎1 ≥ 휎2 ≥ 휎3 ≥ ⋯ ≥ 휎푟 ≥ 휎푟+1 ≥ 0, 휎푗 is singular value 푘 = 2,3, … ,2푁𝑔 8) Plotting the singular values of matrix. This step is used to take the nonzero singular values VI. THE PROPOSED METHOD 9) Determining the estimation of 푤푗.

The data used in this research are the secondary data of 푈 = [푢1 푢2 ⋯ 푢푛] Magnetic Resonance Image (MRI) of brain taken from the hospital of Panti Rapih Yogyakarta, Indonesia. The used data 푢 푢 푢 are 114 brain MRI where 57 data are normal images and 57 11 12 1푛 푢21 푢22 푢2푛 data are cancer images. In this research, the data are analyzed where 푢1 = [ ⋮ ], 푢2 = [ ⋮ ], 푢푁 = [ ⋮ ], using RBFNN with singular value decomposition method. 푢푛1 푢푛2 푢푛푛 The procedure to diagnose brain cancer using RBFNN-SVD model is shown in Fig. 2. 푇 ⋯ 푣 푉 = [푣1 푣2 (푞+1)] where

푣11 푣12 푣1(푞+1) 푣21 푣22 푣2(푞+1) 푣1 = [ ⋮ ], 푣2 = [ ⋮ ], 푣(푞+1) = [ ⋮ ], 푣(푞+1)1 푣(푞+1)2 푣(푞+1)(푞+1)

Furthermore, the estimation of 푤푗⁡ using SVD can be written as (14).

푇 푟 푢푖 푑 푤̂ = ∑푖=1 푣푖 (14) 휎푖 where 푤̂ is vector of weights between hidden layer and output layer, 푑 is vector of target on the training data, 푢푖⁡is i

th column of matrix 푈,⁡휎푖 is i th singular value with 휎푖 > 푇 ⁡0,⁡푣푖 is i th column of matrix 푉 , 푟 is number of nonzero singular values taken.

10) Determining the output of RBFNN-SVD model The estimation 푤̂ in (14) is used to estimate 푤푗 and we get the RBFNN model as (15).

Fig. 2. The diagram of diagnosing brain cancer using RBFNN-SVD. 푞 푦̂ = ∑푗=1 푤̂푗휑푗(푥) + 푤0 (15) Estimation of parameters of RBFNN model using SVD is done by the following steps. 1) Preprocessing of data. VII. RESULTS AND DISCUSSION 2) Image extraction using GLCM. In this section, we apply the proposed method to diagnosis 3) Determining the input and target variables. brain cancer as following steps. First, we determine the 4) Dividing of data to training and testing data. preprocessing data. In this process, the cropping image of 5) Normalizing the data MRI is done. Fig. 3 shows the original image of MRI and the 6) Learning of RBFNN. In this process, the parameters resulted image processing. of radial basis function are determined by K-mean clustering. The weights of NN are determined by SVD method. 7) Determining the optimal weights using SVD.

푇 흋푛×(푞+1) = 푈푛×푛훴푛×(푞+1)푉(푞+1)×(푞+1) (12) where⁡푛 is the number of training data,⁡푞⁡is⁡the number of cluster,⁡흋⁡is a matrix of Gaussian activation function for (a) (b) Fig. 3. (a) original image, (b) result of image preprocessing.

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Then, the image extraction process is done by using GLCM method and there are 14 extractions resulted which 푤̂0 0,56695 are energy, contrast, correlation, sum of squares, inverse 푤̂1 1,90672

푤̂2 −2,95405 difference moment, sum average, sum variance, sum entropy, 푤̂ −3,33099 entropy, difference variance, entropy difference, maximum 3 푤̂ −16 4 8,63 × 10 probability, homogeneity and dissimilarity. 풘̂ = 푤̂5 = 0,84061 (16) The input variables of the RBFNN are 14 extractions 푤̂ 0,40070 6 resulted and the targets of RBFNN are normal and abnormal 푤̂ −0,00086 7 brain. In this study, we divide the data to training and testing −3,48536 푤̂8 data. There are 90 training data and 24 testing data, and then 푤̂9 0,557821 [ 1,16185 ] the data are normalized. Then, learning process of RBFNN is [푤̂10] done by three steps: the K-mean clustering method to get cluster center and distance, determining the number of hidden Based on the weight (16), we have the RBFNN-SVD neurons by using trial, and then determining the weight of model as follows. RBFNN by using SVD method. Based on the training data, 10 we set the matrix 흋 using (5). The size of matrix 흋 is 90 × 푦 = ∑ 풘̂푗휑푗(풙) + 푤0 11 due to the number of training data 90 and the number of 푗=1 cluster 10. Furthermore, the SVD method is applied to the In this research, we also estimate the weights of the RBFNN matrix⁡흋. The singular values in the diagonal matrix from ith model using back propagation (BP) algorithm. BP algorithm row to 11th row are positive and in other elements are zero. is the most popular learning process in neural networks. The Fig. 4 shows the positive singular values of matrix⁡흋. Based BP is performed only between hidden layer and output layer. on Fig. 4, all nonzero singular values are used to determine The performance of the RBFNN using SVD and BP the weights between hidden layer and output layer (14). algorithm is presented in Table II.

TABLE II: COMPARISON OF ACCURACY, SENSITIVITY, AND SPECIFICITY FOR TRAINING AND TESTING DATA USING RBFNN WITH BACK PROPAGATION AND SVD METHODS Method Training Testing Running (%) (%) times (s) Sensitivity 84.4 100 Backpropagation Specificity 77.8 83.3 7.087 Accuracy 81.1 91.7 Sensitivity 84.4 100 SVD Specificity 77.8 83.3 3.134 Accuracy 81.1 91.7

Based on the Table II, both models have the same accuracy, sensitivity and specificity for training and testing data. But, Fig. 4. The singular values of matrix of Gaussian activation function for the running times of SVD method is less than BP method. training data. This result indicates that SVD is more efficient than BP

method. Table I shows the model accuracy for some clusters. Based on the Table I, the highest accuracy for training and testing data is achieved at 81.1% and 91.7%, respectively for ten VIII. CONCLUSION clusters. Therefore, the optimal number of cluster is ten clusters which are the number of hidden neurons. The RBFNN model can be used for brain cancer diagnosis using MRI image data. The diagnostic steps of brain cancer TABLE I: COMPARISON OF MODEL ACCURACY FOR SOME CLUSTERS begin with MRI image processing and image extraction using Number of Training data Testing data GLCM. Image extraction results are used as input variables cluster (%) (%) whereas target variables use classification of normal and brain 2 57.8 66.7 cancer. The data are divided into two sets, namely the training 3 53.3 54.2 and testing data. Then, the input are normalized. The RBFNN 4 65.6 58.3 5 64.4 54.2 are learned to determine the center and distance of Gaussian 6 65.6 75 activation function using K-means clustering method and to 7 58.9 58.3 calculate the weight between hidden layer and output layer 8 67.8 62.5 9 70 66.7 using back propagation and SVD methods. The performance 10* 81.1 91.7 of the RBFNN model can be considered based on its sensitivity, 11 78.9 87.5 specificity, and accuracy values. The results show that the 12 80 87.5 13 78.9 87.5 sensitivity, specificity, and accuracy of RBFNN diagnoses 14 50 50 with backpropagation equals to those of RBFNN with SVD. 15 73.3 75 However, the RBFNN-SVD delivers an advantage in the running speed of the program. In the next study, to increase Then, by using (14), we have the weight of RBFNN as model accuracy, we will add hidden layer as fuzzification of follows. input. Then, we will apply fuzzy C-mean clustering (FCM)

531 International Journal of Machine Learning and Computing, Vol. 9, No. 4, August 2019 method to get optimal cluster center and to determine the [17] A. M. Abadi, D. U. Wutsqa, and L. R. Pamungkas, “Detection of lung optimal number of hidden neurons. cancer using radiograph images enhancement and radial basis function classifier,” in Proc. 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, REFERENCES CISP-BMEI, October 2017. [18] D. B. Andrew, Radial Basis Function. Handbook of Neural Network, [1] HPV Information Center. Humans Papillomavirus and Related Signal Processing, 2002. Diseases Report INDONESIA. [Online] Available: [19] R. A. Johnson and D. W. Wichern, Applied Multivariate Statistical http://www.hpvcenter.net Analysis, 6th ed., Upper Saddle River, New Jersey: Pearson, 2007. [2] Kementrian Kesehatan Republik Indonesia. Pedoman Nasional [20] J. T. Scheick, Linear Algebra with Applications, Singapore: Pelayanan Dokter Tumor Otak. [Online] Available: McGraw-Hill, 1997. http://www.depkes.go.id [21] W. Zhu, N. Zeng, and N. Wang, “Sensitivity, specificity, accuracy, [3] A. A. Kathalkar, R. S. Kawitkar, and A. Chopade, “Anficial neural associated confidence interval and ROC analysis with practical SAS® network based brain cancer analysis and classification,” International implementations,” Health Care and Life Sciences, pp. 1-9, 2010. Journal of Computer Application, vol. 66, no. 10, pp. 40-43, 2013. [22] D. Gadkari, “Image quality analysis using GLCM,” Thesis, University [4] K. U. Rathod, and Y. D. Kapse, “MATLAB based brain tumor of Central Florida, 2004. extraction using artificial neural network,” International Journal in [23] R.M. Haralick, K. Shanmugam, and I. Dinstein, “Textural features for Recent and Innovation Treand in Comuting and Communication, vol. 4, image classification,” IEEE Transactions on Systems, Man and no. 3, pp. 360-364, 2016. Cybernetics, vol. SMC-3, no. 6, pp. 610-621, 1973. [5] G. R. Nayak, and T. Verma, “Brain cancer classification using back [24] D. U. Wutsqa and M. Marwah, “Median filter noise reduction of image propagation neural network and principle component analysis,” and backpropagation neural network model for cervical cancer International Journal of Technical Research and Application, vol. 2, classification,” Journal of Physics: Conference Series, vol. 855, 2017. no. 4, pp. 26-31, 2014. [25] D. U. Wutsqa and P. S. Rahmada, “Point operation to enhance the [6] P. V. Ramaraju, and S. Baji, “Brain tumour classification, detection performance of fuzzy neural network model for breast cancer and segmentation using digital image processing and probabilistic classification,” Journal of Engineering and Applied Science, vol. 12, neural network techniques,” International Journal of Emerging Trends no. 17, pp. 4405-441, 2017 in Electrical and Electronics, vol. 10, no. 10, pp. 15-20, 2014. [26] D. U. Wutsqa and C. L. R. Mandadara,” Lung cancer classification [7] N. D. Pergad, and K. V. Shingare, “Brain MRI image classification using radial basis function neural network model with point operation,” using probabilistic neural network and tumor detection using image in Proc. 10th International Congress on Image and Signal Processing, segmentation,” International Journal of Advanced Research in BioMedical Engineering and Informatics, CISP-BMEI, October 2017. Computer Engineering and Technology, vol. 4, no. 6, pp. 2946-2951, [27] O. B. Abouelatta, “Classification of copper alloys microstructure using 2015. image processing and neural network,” Journal of America Science, [8] B. Al-Naami, M. A. Mallouh, and E. A. Hafez, “Performance vol. 9, pp. 213-223, 2013 comparison of adaptive neural networks and adaptive neuro-fuzzy [28] A. B. Girisha, M. C. Chandrashekhar, and M. Z. Kurian, “Texture inference system in brain cancer classification,” Jordan Journal of feature extraction of video frames using GLCM,” International Mechanical and Industrial Engineering, vol. 8, no. 5, pp. 305-312, Journal of Engineering Trends and Technology, vol. 4, pp. 2718-2721, 2014. 2013. [9] N. Meghana, and K. R. Rekha, “Artificial neural network based classification of brain tumor from MRI using FCM and bounding method,” Internationl Journal of Engineering Research & Technology, Agus Maman Abadi received his Dr. degree from vol. 4, no. 5, pp. 982-985, 2015. Gadjah Mada University, Indonesia in 2010. Presently, [10] S. Suhag and L. M. Saini, “Automatic brain tumor detection and nd he is an associate professor of Mathematics classification using SVM classifier,” in Proc. ISER 2 International department, Yogyakarta State University, Indonesia. Conference, Singapore, 2015, pp. 55-59. His current research interests include fuzzy [11] S. Roy, S. Sadhu, S. K. Bandyopadhyay, D. Bhattacharyya, and T. H. model, fuzzy systems, neural network model, image Kim, “Brain tumor classification using adaptive neuro-fuzzy inference processing, and fuzzy decision making. system from MRI,” International Journal of Bio-Science and

Bio-Technology, vol. 8, no. 3, pp. 203-218, 2016. [12] T. J. Devadas, and R. Ganesan, “Analysis of CT brain images using radial basis function neural network,” Defence Science Journal, vol. 62, no. 4, pp. 212-218, 2012. Dhoriva Urwatul Wustqa received her Dr. degree [13] P. Padmapriya, K. Manikandan, K. Jeyanthi, V. Renuga, and J. from Gadjah Mada University, Indonesia in 2008. Sivaraman, “Detection and classification of brain tumor using radial Presently, she is an associate professor of Mathematics basis function,” Indian Journal of Science and Technology, vol 9, no. 1, Department, Yogyakarta State University, Indonesia. pp.1-5, 2016. Her current research interests include fuzzy time series [14] P. Jaganathan, N. Rajkumar, and R. Kuppuchamy, “A comparative model, neural network model, and image processing. study of improved F-score with support vector machine and RBF network for breast cancer classification,” International Journal of Machine Learning and Computing, vol. 2, no. 6, pp. 741-745, 2012.

[15] S. Hamena and S. Meshoul, “Multi-class classification of gene Nurhayadi received his Dr. degree from Gadjah Mada expression data using deep learning for cancer prediction,” University, Indonesia, in 2014. Presently, he is an International Journal of Machine Learning and Computing, vol. 8, no. associate professor of Mathematics Education 5, pp. 454-459, 2018. Department, Tadulako University, Indonesia. His [16] H. Yoshimura, T. Shimizu, M. Hori, Y. Iwai, and S. Kishid, “EEG current research interests include fuzzy time series signals classification by using an ensemble TPunit neural networks for model, fuzzy systems, and neural network model. the diagnosis of epilepsy,” International Journal of Machine Learning and Computing, vol. 2, no. 6, pp. 758-761, 2012.

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