Unsupervised Detection of Suspicious Tissue Using Data Modeling and PCA

Abdel‐Qader, Ikhlas; Shen, Lixin; Jacobs, Christina; Amara, Fadi Abu; Pashaie-Rad, Sarah

doi:10.1155/ijbi/2006/57850

Cited by 6 publications

(4 citation statements)

References 24 publications

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“…PCA consists of two phases. The first phase finds v uncorrelated and orthogonal vectors; and the second phase projects the testing data into a subspace spanned by these v vectors [14]. PCA algorithm can be presented as follows:…”

Section: Pcamentioning

confidence: 99%

“…Minimum mutual information algorithm [16] is used to estimate Φ(S) as shown in (10)- (14). Equations (10) and (11) are used to estimate the ith central moments and cumulants where E is the expected value and μ is the mean of the current feature r. Equations (12)- (14) are used to estimate Φ(S) (• indicates the Hadamard product of two matrices)…”

Section: Unsupervised Learningmentioning

confidence: 99%

See 1 more Smart Citation

A Computer‐Aided Diagnosis System for Breast Cancer Using Independent Component Analysis and Fuzzy Classifier

Abdel‐Qader

Abu-Amara

2008

Modelling and Simulation in Engineering

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Recommended by Ewa PietkaScreening mammograms is a repetitive task that causes fatigue and eye strain since for every thousand cases analyzed by a radiologist, only 3-4 are cancerous and thus an abnormality may be overlooked. Computer-aided detection (CAD) algorithms were developed to assist radiologists in detecting mammographic lesions. In this paper, a computer-aided detection and diagnosis (CADD) system for breast cancer is developed. The framework is based on combining principal component analysis (PCA), independent component analysis (ICA), and a fuzzy classifier to identify and label suspicious regions. This is a novel approach since it uses a fuzzy classifier integrated into the ICA model. Implemented and tested using MIAS database. This algorithm results in the classification of a mammogram as either normal or abnormal. Furthermore, if abnormal, it differentiates it into a benign or a malignant tissue. Results show that this system has 84.03% accuracy in detecting all kinds of abnormalities and 78% diagnosis accuracy.

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Section: Pcamentioning

confidence: 99%

Section: Unsupervised Learningmentioning

confidence: 99%

A Computer‐Aided Diagnosis System for Breast Cancer Using Independent Component Analysis and Fuzzy Classifier

Abdel‐Qader

Abu-Amara

2008

Modelling and Simulation in Engineering

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“…where i is the number of hidden-layer nodes, i = 1, 2, Á Á Á , h. The original sample and the sample following PCA [34][35][36] were utilized as inputs to the RBFNN, leading to the roadheader cutting performance prediction. The threelayer network theory could be approximated to any nonlinear function 37 ; consequently, the RBFNN model utilized a single hidden-layer structure.…”

Section: Model Comparisonmentioning

confidence: 99%

Cutting performance evaluation of a roadheader machine by PCA and RBF

2018

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As coal mining technology has continuously evolved, gradually the industry has moved toward fully mechanized mining. A roadheader machine is important mechanical equipment for roadway drivage through mechanical crushing. Through analysis and research to discover the key parameters relating to the cutting performance of the roadheader machine, the performance of the roadheader machine must be optimized and costs reduced, as well as productivity increased. As one of the most important statistical methods, principal component analysis (PCA) could not only reduce many factors into fewer overall targets, it could also provide the comparative item weighting, improving the computational efficiency and error precision of the radial basis function neural network, eliminating the correlation of each input variable, and increasing the stability of the network model. The principal variables are determined and a cutting performance evaluation model developed that allow both performance prediction and cutting performance evaluation. From the analysis it is concluded that the primary indicators of the roadheader cutting performance were the unidirectional compressive strength, the cutting resistance fluctuation, the weaving speed of the cutting head, the cutting power fluctuation, and the traction resistance fluctuation. The model is consistent with the practical test results and contributes to discovery of future optimization procedures.

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“…A number of researchers have investigated principal component analysis (PCA), Fisher linear discriminant (FLD), and nearest neighbor classifier (KNN) algorithms. For instance, in [6], Hough transform, PCA, and Euclidean distance were integrated to detect abnormalities in mammograms. In [7], PCA and FLD algorithms were cascaded as dimensionality reduction modules followed by a discriminant analysis classifier.…”

Section: Introductionmentioning

confidence: 99%

A computer aided detection framework for mammographic images using fisher linear discriminant and nearest neighbor classifier

Sarfraz¹,

Abu-Amara

Abdel‐Qader

2012

JBiSE

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Today, mammography is the best method for early detection of breast cancer. Radiologists failed to detect evident cancerous signs in approximately 20% of false negative mammograms. False negatives have been identified as the inability of the radiologist to detect the abnormalities due to several reasons such as poor image quality, image noise, or eye fatigue. This paper presents a framework for a computer aided detection system that integrates Principal Component Analysis (PCA), Fisher Linear Discriminant (FLD), and Nearest Neighbor Classifier (KNN) algorithms for the detection of abnormalities in mammograms. Using normal and abnormal mammograms from the MIAS database, the integrated algorithm achieved 93.06% classification accuracy. Also in this paper, we present an analysis of the integrated algorithm’s parameters and suggest selection criteria

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Unsupervised Detection of Suspicious Tissue Using Data Modeling and PCA

Cited by 6 publications

References 24 publications

A Computer‐Aided Diagnosis System for Breast Cancer Using Independent Component Analysis and Fuzzy Classifier

A Computer‐Aided Diagnosis System for Breast Cancer Using Independent Component Analysis and Fuzzy Classifier

Cutting performance evaluation of a roadheader machine by PCA and RBF

A computer aided detection framework for mammographic images using fisher linear discriminant and nearest neighbor classifier

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