Computer aided diagnosis in digital mammography using combined support vector machine and linear discriminant analyasis classification

Authors:
Mohamed A. Alolfe;Wael A. Mohamed;Abou-Bakr M. Youssef;Ahmed S. Mohamed;Yasser M. Kaddah
Affiliations:
Biomedical Enginerring Department, Cairo University, Egypt;Biomedical Enginerring Department, Cairo University, Egypt;Biomedical Enginerring Department, Cairo University, Egypt;Biomedical Enginerring Department, Cairo University, Egypt;Biomedical Enginerring Department, Cairo University, Egypt
Venue:
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Year:
2009

Citing 6
Cited 0

The nature of statistical learning theory

The nature of statistical learning theory
Learning from Data: Concepts, Theory, and Methods

Learning from Data: Concepts, Theory, and Methods
Digital Image Processing

Digital Image Processing
An introduction to variable and feature selection

The Journal of Machine Learning Research
Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)
Breast cancer diagnosis system based on wavelet analysis and fuzzy-neural

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a computer-aided diagnosis (CAD) system based on combined support vector machine (SVM) and linear discriminant analysis (LDA) classifier for detection and proposed system has been implemented in four stages: (a) Region of interest (ROI) selection of 32×32 pixels size which identifies suspicion regions. (b) Feature extraction stage locally processed image (ROI) to compute the important features of each breast cancer. (c) Feature selection stage by using forward stepwise linear regression method (FSLR). (d) Classification stage, which classify between normal and abnormal patterns and then classify between benign and malignant abnormal. In classification stage, a new method was used, based on combined SVM and LDA classifier (SVM/LDA), and compared to other classifiers such as SVM, LDA, and fuzzy C-mean (FCM) classifiers. The proposed system was shown to have a large potential for breast cancer diagnostic in digital mammograms.