Support vector machine-based image classification for genetic syndrome diagnosis

Authors:
Amit David;Boaz Lerner
Affiliations:
Pattern Analysis and Machine Learning Lab, Department of Electrical and Computer Engineering, Ben-Gurion University of the Negev, P.O. Box 653, 84105 Beer-Sheva, Israel;Pattern Analysis and Machine Learning Lab, Department of Electrical and Computer Engineering, Ben-Gurion University of the Negev, P.O. Box 653, 84105 Beer-Sheva, Israel
Venue:
Pattern Recognition Letters
Year:
2005

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Abstract

We implement structural risk minimization and cross-validation in order to optimize kernel and parameters of a support vector machine (SVM) and multiclass SVM-based image classifiers, thereby enabling the diagnosis of genetic abnormalities. By thresholding the distance of patterns from the hypothesis separating the classes we reject a percentage of the miss-classified patterns reducing the expected risk. Accurate performance of the SVM in comparison to other state-of-the-art classifiers demonstrates the benefit of SVM-based genetic syndrome diagnosis.