Fuzzy clustering complex-valued neural network to diagnose cirrhosis disease

Ceylan, Rahime; Ceylan, Murat; Özbay, Yüksel; Kara, Sadık

doi:10.1016/j.eswa.2011.02.025

Cited by 41 publications

(34 citation statements)

References 19 publications

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“…In parallel with the increase in studies on the modeling of non-linear systems in computer media, ANNs have become widely applied and represent a constantly changing and developing structure through newly developing methods. Some examples of fields of application of ANN include computations made by learning a linear and non-linear function and using this function, image processing applications, sound identification applications, digital data analysis, and result-obtaining processes in statistical approaches [15].…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

An approach for tissue density classification in mammographic images using artificial neural network based on wavelet and curvelet transforms

Yaşar¹,

Ceylan

2015

SPIE Proceedings

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Breast cancer is one of the types of cancer which is most commonly seen in women. Density of breast is an important indicator for the risk of cancer. In addition, densities of tissue may harden the diagnosis by hiding the abnormalities occurring on the breast. For this reason, during the process of diagnosis, the process of automatic classification of breast density has a significant importance. In this study, a new system with the base of Artificial Neural Network (ANN) and multiple resolution analysis is suggested. Wavelet and curvelet analyses having the most common use have been used as multi resolution analysis. 4 pieces of statistics which are minimum value, maximum value, mean value and standard deviation have been extracted from the images which have been eluted to their sub-bands via multi resolution analysis. For the purpose of testing the success of the system, 322 pieces of images which are in MIAS database have been used. The obtained results for different backgrounds are so satisfying; and the highest classification values have been obtained as 97.16 % with Wavelet transform and ANN for fatty background and 79.80 % with Wavelet transform and ANN for fatty-glanduar background. The same results have been obtained using Wavelet transform and ANN and Curvelet transform and ANN for dense background and accuracy rate of 84.82 % have been reached. The results of mean classification have been obtained, for three pieces of tissue types (fatty, fatty-glanduar, dense), in sequence as 84.47 % with the use of ANN, 85.71 % with the use of curvelet analysis and ANN; and 87.26 % with the use of wavelet analysis and ANN.

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Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

An approach for tissue density classification in mammographic images using artificial neural network based on wavelet and curvelet transforms

Yaşar¹,

Ceylan

2015

SPIE Proceedings

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“…In Sect. 4, two examples are given to demonstrate the effectiveness of the proposed optimal control scheme. In Sect.…”

Section: Introductionmentioning

confidence: 99%

“…For example, complex-valued nonlinear equalizer was proposed in nonlinear channel equalization of wireless communication systems, based on pipelined decision feedback recurrent neural network in [3]. Fuzzy clustering complex-valued neural network was proposed to classify portal vein Doppler signals for biomedical pattern classification in [4]. By Lyapunov function in complex fields, the stability criteria of complex-valued impulsive system were established in [5].…”

Section: Introductionmentioning

confidence: 99%

Off-policy neuro-optimal control for unknown complex-valued nonlinear systems based on policy iteration

Song

Wei

Xiao

2016

Neural Comput & Applic

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This paper establishes an optimal control of unknown complex-valued system. Policy iteration is used to obtain the solution of the Hamilton-Jacobi-Bellman equation. Off-policy learning allows the iterative performance index and iterative control to be obtained by completely unknown dynamics. Critic and action networks are used to get the iterative control and iterative performance index, which execute policy evaluation and policy improvement. Asymptotic stability of the closed-loop system and the convergence of the iterative performance index function are proven. By Lyapunov technique, the uniformly ultimately bounded of the weight error is proven. Simulation study demonstrates the effectiveness of the proposed optimal control method.

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“…Medical European studies have shown that a percent varying between 20% -30% of the population from developed countries is suffered from fatty liver disease [3]. The standard way to diagnose fatty liver disease is liver biopsy, however, the invasiveness and cost of the procedure limits its widespread use [4]. Therefore, medical imaging techniques such as Ultrasound (US), Computed Tomography (CT) and Magnetic Resonance Imaging (MRI), that are noninvasive diagnostic methods, are used for examination.…”

Section: Introductionmentioning

confidence: 99%

Fully automatic segmentation and classification of liver ultrasound images using completed LBP texture features

Owjimehr

Danyali

Helfroush

2014

2014 22nd Iranian Conference on Electrical Engineering (ICEE)

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In this paper, a novel method is presented to discriminate fatty, normal and heterogeneous livers based on textural analysis of liver ultrasound images using Completed Local Binary Pattern (CLBP). The proposed approach is able to fully automatically select the optimum regions of interest (ROIs) of the liver images. These optimum ROIs are analyzed to extract CLBP features. A support vector machine (SVM) classifier is then employed to classify the fatty, normal and heterogeneous livers. The fully automatic scheme to select the ROIs with low computational cost and CLBP to extract texture features clearly illustrates the efficiency of this system. The results showed the overall accuracy of 98.67% with sensitivity of 100% for fatty and heterogeneous class.

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Fuzzy clustering complex-valued neural network to diagnose cirrhosis disease

Cited by 41 publications

References 19 publications

An approach for tissue density classification in mammographic images using artificial neural network based on wavelet and curvelet transforms

An approach for tissue density classification in mammographic images using artificial neural network based on wavelet and curvelet transforms

Off-policy neuro-optimal control for unknown complex-valued nonlinear systems based on policy iteration

Fully automatic segmentation and classification of liver ultrasound images using completed LBP texture features

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