A Kernel-Based Intuitionistic Fuzzy C-Means Clustering Using a DNA Genetic Algorithm for Magnetic Resonance Image Segmentation

Zang, Wenke; Zhang, Weining; Zhang, Wenqian; Liu, Xiyu

doi:10.3390/e19110578

Cited by 19 publications

(8 citation statements)

References 43 publications

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“…The major advantage of M‐ARKFCM is very robust to cluster parameters that helps to decrease the computational charges and also it gives better results for overlapped data‐sets. The modified‐ARKFCM approach employs the heterogeneity of grayscales in the neighborhood for measuring the local contextual information . In modified‐ARKFCM approach, the standard Euclidean distance is replaced with correlation function.…”

Section: Proposed Methodologymentioning

confidence: 99%

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Detection and classification of normal and abnormal patterns in mammograms using deep neural network

Suresh

Rao

Reddy

2019

Concurrency and Computation

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Summary Breast cancer detection is the most challenging aspect in the field of health monitoring system. In this paper, breast cancer detection was assessed by employing Mammographic Image Analysis Society (MIAS) dataset. The proposed approach contains four major steps, namely, image‐preprocessing, segmentation, feature extraction, and classification. Initially, Laplacian filtering was utilized to identify the area of edges in mammogram images and, also, it was very sensitive to noise. Then, segmentation was carried‐out using modified‐Adaptively Regularized Kernel‐based Fuzzy‐C‐Means (ARKFCM); it was a flexible high level machine learning technique to localize the object in complex template. In conventional ARKFCM, it was hard to segment the ill‐defined masses in mammogram images. To address this concern, the Euclidean distance in ARKFCM was replaced by correlation function in order to improve the segmentation efficiency. The hybrid feature extraction (Histogram of Oriented Gradients (HOG), homogeneity, and energy) was performed on the segmented cancer region to extract feature subsets. The respective feature values were given as the input for a multi‐objective classifier: Deep Neural Network (DNN) for classifying the normal and abnormal regions in mammogram images. The experimental outcome shows that the proposed methodology improved accuracy in breast cancer classification up to 3% to 9% compared to other existing methods.

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Section: Proposed Methodologymentioning

confidence: 99%

“…The modified-ARKFCM approach employs the heterogeneity of grayscales in the neighborhood for measuring the local contextual information. 24 In modified-ARKFCM approach, the standard Euclidean distance is replaced with correlation function. Figure 3 represents the pre-processed mammogram image and segmented image.…”

Section: Segmentation Using Modified-arkfcmmentioning

confidence: 99%

Detection and classification of normal and abnormal patterns in mammograms using deep neural network

Suresh

Rao

Reddy

2019

Concurrency and Computation

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“…In the present research, the GA solver of MATLAB's Global Optimization Toolbox (version 2015a) was utilized maintaining without changes its already optimized options. See, e.g., [12], for a recent application to system identification and [13] for an application to magnetic resonance image segmentation.…”

Section: Comparison With the Genetic And The Simulated-annealing Algomentioning

confidence: 99%

An Improved Chaotic Optimization Algorithm Applied to a DC Electrical Motor Modeling

Fiori

Filippo

2017

Entropy

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The chaos-based optimization algorithm (COA) is a method to optimize possibly nonlinear complex functions of several variables by chaos search. The main innovation behind the chaos-based optimization algorithm is to generate chaotic trajectories by means of nonlinear, discrete-time dynamical systems to explore the search space while looking for the global minimum of a complex criterion function. The aim of the present research is to investigate the numerical properties of the COA, both on complex optimization test-functions from the literature and on a real-world problem, to contribute to the understanding of its global-search features. In addition, the present research suggests a refinement of the original COA algorithm to improve its optimization performances. In particular, the real-world optimization problem tackled within the paper is the estimation of six electro-mechanical parameters of a model of a direct-current (DC) electrical motor. A large number of test results prove that the algorithm achieves an excellent numerical precision at a little expense in the computational complexity, which appears as extremely limited, compared to the complexity of other benchmark optimization algorithms, namely, the genetic algorithm and the simulated annealing algorithm.

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“…Zadeh [ 1 ] introduced the concept of the fuzzy set to represent uncertain data, which is abounding in the real world, and it was extended by Atanassov [ 2 ] to the intuitionistic fuzzy set. These sets have been applied to vague soft sets [ 3 , 4 , 5 ] and many real-life problems in uncertain and ambiguous environment [ 6 , 7 , 8 ]. The words neutrosophy and neutrosophic were coined by Smarandache [ 9 ], who later defined neutrosophic sets [ 10 ] as natural extensions of fuzzy and intuitionistic fuzzy sets.…”

Section: Introductionmentioning

confidence: 99%

The Complex Neutrosophic Soft Expert Relation and Its Multiple Attribute Decision-Making Method

Al-Quran

Hassan

2018

Entropy

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This paper introduces a novel soft computing technique, called the complex neutrosophic soft expert relation (CNSER), to evaluate the degree of interaction between two hybrid models called complex neutrosophic soft expert sets (CNSESs). CNSESs are used to represent two-dimensional data that are imprecise, uncertain, incomplete and indeterminate. Moreover, it has a mechanism to incorporate the parameter set and the opinions of all experts in one model, thus making it highly suitable for use in decision-making problems where the time factor plays a key role in determining the final decision. The complex neutrosophic soft expert set and complex neutrosophic soft expert relation are both defined. Utilizing the properties of CNSER introduced, an empirical study is conducted on the relationship between the variability of the currency exchange rate and Malaysian exports and the time frame (phase) of the interaction between these two variables. This study is supported further by an algorithm to determine the type and the degree of this relationship. A comparison between different existing relations and CNSER to show the ascendancy of our proposed CNSER is provided. Then, the notion of the inverse, complement and composition of CNSERs along with some related theorems and properties are introduced. Finally, we define the symmetry, transitivity and reflexivity of CNSERs, as well as the equivalence relation and equivalence classes on CNSESs. Some interesting properties are also obtained.

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A Kernel-Based Intuitionistic Fuzzy C-Means Clustering Using a DNA Genetic Algorithm for Magnetic Resonance Image Segmentation

Cited by 19 publications

References 43 publications

Detection and classification of normal and abnormal patterns in mammograms using deep neural network

Detection and classification of normal and abnormal patterns in mammograms using deep neural network

An Improved Chaotic Optimization Algorithm Applied to a DC Electrical Motor Modeling

The Complex Neutrosophic Soft Expert Relation and Its Multiple Attribute Decision-Making Method

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