Suitable MLP Network Activation Functions for Breast Cancer and Thyroid Disease Detection

Isa, Iza Sazanita; Saad, Zuraidi; Omar, Siti Nazirah; Osman, Muhammad Khusairi; Ahmad, Khairul Azman; Sakim, Harsa Amylia Mat

doi:10.1109/cimsim.2010.93

Cited by 55 publications

(30 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…MLP is the formation of artificial nerve system that are mostly used in terms of education and application [19]. MLP has abilities to learn and give the better performance of classification are proven in a number of research [10] [20]. At the classification stage, this research used MLP method by using three layers, consisting of input layer, hidden layer, and output layer.…”

Section: Classificationmentioning

confidence: 99%

Texture feature extraction for the lung lesion density classification on computed tomography scan image

Hasnely

Nugroho²,

Wibirama³

et al. 2016

CST

View full text Add to dashboard Cite

The radiology examination by computed tomography (CT) scan is an early detection of lung cancer to minimize the mortality rate. However, the assessment and diagnosis by an expert are subjective depending on the competence and experience of a radiologist. Hence, a digital image processing of CT scan is necessary as a tool to diagnose the lung cancer. This research proposes a morphological characteristics method for detecting lung cancer lesion density by using the histogram and GLCM (Gray Level Co-occurrence M atrices). The most well-known artificial neural network (ANN) architecture that is the multilayers perceptron (M LP), is used in classifying lung cancer lesion density of heterogeneous and homogeneous. Fifty CT scan images of lungs obtained from the Department of Radiology of RSUP Dr. Sardjito Hospital, Yogyakarta are used as the database. The results show that the proposed method achieved the accuracy of 98%, sensitivity of 96%, and specificity of 96%.

show abstract

Section: Classificationmentioning

confidence: 99%

Texture feature extraction for the lung lesion density classification on computed tomography scan image

Hasnely

Nugroho²,

Wibirama³

et al. 2016

CST

View full text Add to dashboard Cite

show abstract

“…Through extensive simulations, the authors prove that such network shows great performance in comparison to analogous FFNN with identical sigmoid AFs. In [22], the authors propose a performance comparison between eight different AFs, including the stochastic AF and the novel “neural” activation function, obtained by the combination of a sinusoidal and sigmoid activation function. Two tests sets are used for comparison: breast cancer and thyroid diseases related data.…”

Section: Activation Functions For Easy Trainingmentioning

confidence: 99%

On Training Efficiency and Computational Costs of a Feed Forward Neural Network: A Review

Laudani

Lozito

Fulginei

et al. 2015

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

A comprehensive review on the problem of choosing a suitable activation function for the hidden layer of a feed forward neural network has been widely investigated. Since the nonlinear component of a neural network is the main contributor to the network mapping capabilities, the different choices that may lead to enhanced performances, in terms of training, generalization, or computational costs, are analyzed, both in general-purpose and in embedded computing environments. Finally, a strategy to convert a network configuration between different activation functions without altering the network mapping capabilities will be presented.

show abstract

“…Researchers in various studies have corroborated that the polynomial and spline activation functions have the potential to estimate the highly non-linear systems [20][21][22][23]. Many other works imply that selection of activation functions is dependent on the application [24,25]. Consequently and as a new point of view, if we choose the arbitrary function f (x) as the activation function in the last layer, so that f (x) would meet the just mentioned conditions, then as the energy of the network approaches its minimum, the output of f (x) will converge to its desired objective.…”

Section: Basic Concepts: To Solve An Objective Function Using Unidimementioning

confidence: 99%

Multi-Objective Optimization by Means of Multi-Dimensional MLP Neural Networks

Rafei¹,

Sorkhabi²,

Mosavi³

2014

NNW

View full text Add to dashboard Cite

In this paper, a multi-layer perceptron (MLP) neural network (NN) is put forward as an efficient tool for performing two tasks: 1) optimization of multi-objective problems and 2) solving a non-linear system of equations. In both cases, mathematical functions which are continuous and partially bounded are involved. Previously, these two tasks were performed by recurrent neural networks and also strong algorithms like evolutionary ones. In this study, multi-dimensional structure in the output layer of the MLP-NN, as an innovative method, is utilized to implicitly optimize the multivariate functions under the network energy optimization mechanism. To this end, the activation functions in the output layer are replaced with the multivariate functions intended to be optimized. The effective training parameters in the global search are surveyed. Also, it is demonstrated that the MLP-NN with proper dynamic learning rate is able to find globally optimal solutions. Finally, the efficiency of the MLP-NN in both aspects of speed and power is investigated by some well-known experimental examples. In some of these examples, the proposed method gives explicitly better globally optimal solutions compared to that of the other references and also shows completely satisfactory results in other experiments.

show abstract

Suitable MLP Network Activation Functions for Breast Cancer and Thyroid Disease Detection

Cited by 55 publications

References 13 publications

Texture feature extraction for the lung lesion density classification on computed tomography scan image

Texture feature extraction for the lung lesion density classification on computed tomography scan image

On Training Efficiency and Computational Costs of a Feed Forward Neural Network: A Review

Multi-Objective Optimization by Means of Multi-Dimensional MLP Neural Networks

Contact Info

Product

Resources

About