Automatic detection of the breast border and nipple position on digital mammograms using genetic algorithm for asymmetry approach to detection of microcalcifications

Karnan, M.; Thangavel, K.

doi:10.1016/j.cmpb.2007.04.007

Cited by 57 publications

(22 citation statements)

References 9 publications

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“…In case of ROC analysis, the area under the ROC curve (A Z ) is used to measure the accuracy of classifier [37]. A Z is in the range between 0.0 and 1.0.…”

Section: Measures For Classifier Accuracymentioning

confidence: 99%

Classification of Clustered Microcalcifications using MLFFBP-ANN and SVM

Khehra

Pharwaha

2016

Egyptian Informatics Journal

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The classifier is the last phase of Computer-Aided Diagnosis (CAD) system that is aimed at classifying Clustered Microcalcifications (MCCs). Classifier classifies MCCs into two classes. One class is benign and other is malignant. This classification is done based on some meaningful features that are extracted from enhanced mammogram. A number of classifiers have been proposed for CAD system to classify MCCs as benign or malignant. Recently, researchers have used Artificial Neural Networks (ANNs) as classifiers for many applications. Multilayer Feed-Forward Backpropagation (MLFFB) is the most important ANN that has been successfully used by researchers to solve various problems. Similarly, Support Vector Machines (SVMs) belong to another category of classifiers that researchers have recently given considerable attention. So, to explore MLFFB and SVM classifiers for MCCs classification problem, in this paper, Levenberg-Marquardt Multilayer FeedForward Backpropagation ANN (LM-MLFFB-ANN) and Sequential Minimal Optimization (SMO) based SVM (SMO-SVM) are used for the classification of MCCs. Thus, a comparative evaluation of the relative performance of LM-MLFFBP-ANN and SMO-SVM is investigated to classify MCCs as benign or malignant. For this comparative evaluation, first suitable features are extracted from mammogram images of DDSM database. After this, suitable features are selected using Particle Swarm Optimization (PSO). At the end, MCCs are classified using LM-MLFFBP-ANN and SMO-SVM classifiers based on the selected features. Confusion matrix and ROC analysis are used to

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“…In case of ROC analysis, the area under the ROC curve (A Z ) is used to measure the accuracy of classifier [37]. A Z is in the range between 0.0 and 1.0.…”

Section: Measures For Classifier Accuracymentioning

confidence: 99%

Classification of Clustered Microcalcifications using MLFFBP-ANN and SVM

Khehra

Pharwaha

2016

Egyptian Informatics Journal

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“…It is developed by Simon [14]. Such population based stochastic optimization algorithms have been successfully applied to solve a variety of problems that are related to various fields such as image processing [17], pattern recognition [18], optimization of objective functions [19], wireless sensor networks [20], machine learning [21], design and economic optimization of shell-and-tube heat exchangers [22] and optimal job scheduling in cloud computing [23]. It has been observed that the performance of these biologically inspired metaheuristic algorithms is better than the classical optimization approaches.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Genetic Algorithm, Particle Swarm Optimization and Biogeography-based Optimization for Feature Selection to Classify Clusters of Microcalcifications

Khehra

Pharwaha

2016

J. Inst. Eng. India Ser. B

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Ductal carcinoma in situ (DCIS) is one type of breast cancer. Clusters of microcalcifications (MCCs) are symptoms of DCIS that are recognized by mammography. Selection of robust features vector is the process of selecting an optimal subset of features from a large number of available features in a given problem domain after the feature extraction and before any classification scheme. Feature selection reduces the feature space that improves the performance of classifier and decreases the computational burden imposed by using many features on classifier. Selection of an optimal subset of features from a large number of available features in a given problem domain is a difficult search problem. For n features, the total numbers of possible subsets of features are 2 n . Thus, selection of an optimal subset of features problem belongs to the category of NP-hard problems. In this paper, an attempt is made to find the optimal subset of MCCs features from all possible subsets of features using genetic algorithm (GA), particle swarm optimization (PSO) and biogeography-based optimization (BBO). For simulation, a total of 380 benign and malignant MCCs samples have been selected from mammogram images of DDSM database. A total of 50 features extracted from benign and malignant MCCs samples are used in this study. In these algorithms, fitness function is correct classification rate of classifier. Support vector machine is used as a classifier. From experimental results, it is also observed that the performance of PSO-based and BBO-based algorithms to select an optimal subset of features for classifying MCCs as benign or malignant is better as compared to GA-based algorithm.

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“…The main issues in developing feature selection, also known as dimensionality reduction techniques, are choosing a small feature set in order to reduce the space and running time of a system, as well as achieving an acceptably high recognition rate. Thangavel et al proposed various feature selection algorithms and compared with the existing algorithm [3,4,8].…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Feature Selection in Digital Mammogram Image Using Tolerance Rough Set Based Quick Reduct

Aroquiaraj

Thangavel

2012

2012 Fourth International Conference on Computational Intelligence and Communication Networks

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Feature Selection (FS) aims to determine a minimal feature subset from a problem domain while retaining a suitably high accuracy in representing the original features. Rough set theory (RST) has been used as such a tool with much success. In the supervised FS methods, various feature subsets are evaluated using an evaluation function or metric to select only those features which are related to the decision classes of the data under consideration. However, for many data mining applications, decision class labels are often unknown or incomplete, thus indicating the significance of unsupervised feature selection. However, in unsupervised learning, decision class labels are not provided. The problem is that not all features are important. Some of the features may be redundant, and others may be irrelevant and noisy. In this paper, a novel unsupervised feature selection in mammogram image, using tolerance rough set based quick reduct, is proposed. A typical mammogram image processing system generally consists of mammogram image acquisition, pre-processing of image segmentation, feature extraction, feature selection and classification. The proposed method is used to select features from the extracted features and the method is compared with existing rough set based supervised feature selection methods. The proposed method is evaluated through classification algorithms in WEKA.

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Automatic detection of the breast border and nipple position on digital mammograms using genetic algorithm for asymmetry approach to detection of microcalcifications

Cited by 57 publications

References 9 publications

Classification of Clustered Microcalcifications using MLFFBP-ANN and SVM

Classification of Clustered Microcalcifications using MLFFBP-ANN and SVM

Comparison of Genetic Algorithm, Particle Swarm Optimization and Biogeography-based Optimization for Feature Selection to Classify Clusters of Microcalcifications

Unsupervised Feature Selection in Digital Mammogram Image Using Tolerance Rough Set Based Quick Reduct

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