Characterization of clustered microcalcifications in mammograms based on support vector machines with genetic algorithms

Wang, Chao; Jiang, Wei; Dong, Xifeng

doi:10.1109/metamat.2006.335011

Cited by 4 publications

(1 citation statement)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A common drawback of CADx systems [8]- [11], which used SVM for pattern classification, is the use of standard formulation of SVM learning [14]. A standard SVM classifier solves the learning problem that is a convex optimization with affine constraints using a quadratic programming (QP) method, which expected to be impractical for handling large-scale studies, optimization the classifier performance and estimating generalization ability using k-fold and leave-one-out cross-validation procedures, embedded feature selection, and hyperparameter selection using exhaustive GA heuristic search [10].…”

Section: Introductionmentioning

confidence: 99%

Classification of Clustered Microcalcifications in Mammograms using Particle Swarm Optimization and Least-Squares Support Vector Machine

Zyout¹

2012

IJCA

View full text Add to dashboard Cite

Feature selection and classifier hyper-parameter optimization are important stages of any computer-aided diagnosis (CADx) system for mammography. The optimal selection for shape features, kernel parameter, and classifier regularization constant is crucial to achieve a good generalization and performance of least-squares support vector machines (LSSVMs). This paper presents a morphology-based CADx that uses a computationally attractive and unified scheme for accomplishing the model selection task.A heuristic parameter search based on particle swarm optimization (PSO) not only reduces the dimensionality of the input feature space but also optimizes hyper-parameters of the classifier. The performance of the proposed shape-based CADx including PSO-LSSVM parameter selection method is examined using 60 microcalcification clusters. Using different cross-validation procedures, the proposed PSO-LSSVM demonstrated a good generalization ability by producing classification accuracies higher than 92%. The best classification accuracy of 97% was obtained using the leave-one-out cross-validation procedure. Comparing the performance of PSO-LSSVM with PSO-SVM method that uses conventional SVM formulation, results demonstrated the attractive computational complexity and classification performance of PSO-LSSVM.

show abstract

Section: Introductionmentioning

confidence: 99%

Classification of Clustered Microcalcifications in Mammograms using Particle Swarm Optimization and Least-Squares Support Vector Machine

Zyout¹

2012

IJCA

View full text Add to dashboard Cite

show abstract

Microcalcification Clusters Detection with Tensor Subspace Learning and Twin SVMs

Zhang

Gao

Wang

2008

2008 7th World Congress on Intelligent Control and Automation

View full text Add to dashboard Cite

This paper presents a novel approach to microcalcification clusters (MCs) detection in mammograms based on the tensor subspace learning and twin support vector machines (TWSVMs). The ground truth of MCs in mammograms is assumed to be known as a priori. First each MCs is enhanced by using a simple artifact removal filter and a well designed high-pass filter. Then the tensor subspace learning algorithms, tensor subspace analysis (TSA) and general tensor discriminant Analysis (GTDA), are employed to extract subspace features. In subspace feature domain, the MCs detection procedure is formulated as a supervised learning and classification problem, and TWSVM is used as a classifier to make decision for the presence of MCs or not. A large number of experiments were carried out to evaluate and compare the performance of the proposed MCs detection algorithms. The experimental results illustrate its effectiveness. Index Terms -microcalcification, twin support vector machines, tensor subspace learning, general tensor discriminant analysis

show abstract

A New Ensemble Learning Approach for Microcalcification Clusters Detection

Zhang¹

2009

JSW

View full text Add to dashboard Cite

A new microcalcification clusters (MCs) detection method in mammograms is proposed, which is based on a new ensemble learning method. In this paper, , we propose a bagging with adaptive cost adjustment ensemble algorithm; and a new ensemble strategy, called boosting with relevance feedback, by embedding the relevance feedback technique into the heterogenous base learner training, and meanwhile carefully design an effectively systematical feedback scheme, which promise the preventing of overfitting. The ground truth of MCs is assumed to be known as a priori. In our algorithm, each MCs is enhanced by a well designed high-pass filter. Then the 116 dimentional image features are extracted by the feature extractor and fed to the ensemble decision model. In image feature domain, the MCs detection procedure is formulated as a supervised learning and classification problem, and the trained ensemble model is used as a classifier to decide the presence of MCs or not. Case study on microcalcification clusters detection for breast cancer diagnosis illustrates that the proposed algorithm is not only effective but also efficient.

show abstract

Characterization of clustered microcalcifications in mammograms based on support vector machines with genetic algorithms

Cited by 4 publications

References 9 publications

Classification of Clustered Microcalcifications in Mammograms using Particle Swarm Optimization and Least-Squares Support Vector Machine

Classification of Clustered Microcalcifications in Mammograms using Particle Swarm Optimization and Least-Squares Support Vector Machine

Microcalcification Clusters Detection with Tensor Subspace Learning and Twin SVMs

A New Ensemble Learning Approach for Microcalcification Clusters Detection

Contact Info

Product

Resources

About