Breast mass classification based on cytological patterns using RBFNN and SVM

Subashini, T. S.; Ramalingam, V.; Palanivel, S.

doi:10.1016/j.eswa.2008.06.127

Cited by 104 publications

(40 citation statements)

References 21 publications

(23 reference statements)

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“…PFC is intended to solve complex classi cation problems with large data sets. Subashini et al [171] have compared the use of polynomial kernel of SVM and RBFNs in ascertaining the diagnostic accuracy of cytological data obtained from the Wisconsin breast cancer database. Their research demonstrates that RBFNs outperformed the polynomial kernel of SVM for correctly classifying the tumors.…”

Section: Rbfns In Classi Cation and Predictionmentioning

confidence: 99%

Radial basis function neural networks: a topical state-of-the-art survey

Dash¹,

Behera²,

Dehuri

et al. 2016

Open Computer Science

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Radial basis function networks (RBFNs) have gained widespread appeal amongst researchers and have shown good performance in a variety of application domains. They have potential for hybridization and demonstrate some interesting emergent behaviors. This paper aims to o er a compendious and sensible survey on RBF networks. The advantages they o er, such as fast training and global approximation capability with local responses, are attracting many researchers to use them in diversi ed elds. The overall algorithmic development of RBF networks by giving special focus on their learning methods, novel kernels, and ne tuning of kernel parameters have been discussed. In addition, we have considered the recent research work on optimization of multi-criterions in RBF networks and a range of indicative application areas along with some open source RBFN tools.

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Section: Rbfns In Classi Cation and Predictionmentioning

confidence: 99%

Radial basis function neural networks: a topical state-of-the-art survey

Dash¹,

Behera²,

Dehuri

et al. 2016

Open Computer Science

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show abstract

“…Sensitivity and specificity are two measures that separately estimate a classifier's performance on different classes [20,21].In terms of medical research, sensitivity is used to measure the percentage of correctly classified benign tumors while specificity is used to measure the percentage of correctly classified malignant tumors.…”

Section: B Performance Measurementioning

confidence: 99%

Comparative Analysis of FeedforwardBackpropagation and Cascade Correlation Algorithm on BUPA Liver Disorder

Jijja¹,

Rai²,

Mathur³

2016

IJET

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Abstract-Neural Network(NN) is widely used in medical research because of its cost effectiveness and easy-to-use systems. NN plays an important role in the decision support system. The sole reason to use the efficient NN model is to manage the rapidly increasing medical data effectively and use these data points for accurate prediction of diseases and thereby providing better health care to the patients. In this paper we have made use of two classification algorithmsi.e. feedforwardbackpropagation neural network(FFBPNN) and Cascade Correlationfeedforward network (CCFFN) and compared the performance of both the algorithms on the basis of sensitivity, specificity and accuracy. The experiment is done on Bupa liver dataset using FFBPNN and CCFFN model using different set of neurons which are trained with Levenberg-Marquardt training algorithm(trainlm) and ResilientBackpropagationtraining algorithm (trainrp). This paper has provided the comparative analysis among different classifiers using two different training algorithmsto find the benign and malignant patients.

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“…For this reason, diagnosing breast cancer automatically has an important place among medical problems. In the literature, various studies have been done towards this aim [1][2][3][4][5][6][7][8]. In [1], support vector machines and radial basis function networks are used for breast mass classification based on cytological patterns.…”

Section: Introductionmentioning

confidence: 99%

“…In the literature, various studies have been done towards this aim [1][2][3][4][5][6][7][8]. In [1], support vector machines and radial basis function networks are used for breast mass classification based on cytological patterns. In [2], association rules are used for dimension reduction for breast cancer dataset and the data obtained is used for input to neural network in order to breast cancer classification of breast cancer.…”

Section: Introductionmentioning

confidence: 99%

Type-2 Fuzzy Multiplication Wavelet Neural Network Model Design for Classification of Breast Cancer

Gündüz

2018

ijarcs

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Breast cancer is one of the most common types of cancer. For this reason, it is very important to diagnose breast cancer. In this paper, a type-2 fuzzy multiplication wavelet neural network model is proposed to classify the Wisconsin Breast Cancer dataset. In this model, Shannon wavelet function is used as the type-2 membership function and the multiplication of the Shannon wavelet functions is used in the conclusion part of the rules. The results of proposed model is compared with type-1 fuzzy multiplication wavelet neural network, multilayer perceptron network, radial basis function network, Bayesian network learning, and decision tree algorithm. It can be seen that proposed type-2 fuzzy multiplication wavelet neural network model gives the best results among these algorithms.

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Breast mass classification based on cytological patterns using RBFNN and SVM

Cited by 104 publications

References 21 publications

Radial basis function neural networks: a topical state-of-the-art survey

Radial basis function neural networks: a topical state-of-the-art survey

Comparative Analysis of FeedforwardBackpropagation and Cascade Correlation Algorithm on BUPA Liver Disorder

Type-2 Fuzzy Multiplication Wavelet Neural Network Model Design for Classification of Breast Cancer

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