SVM Optimization for Hyperspectral Colon Tissue Cell Classification

The classification of normal and malginant colon tissue cells is crucial to the diagnosis of colon cancer in humans. Given the right set of feature vectors, Support Vector Machines (SVMs) have been shown to perform reasonably well for the classification [4, 13]. In this paper, we address the following question: how does the choice of a kernel function and its parameters affect the SVM classification performance in such a system? We show that the Gaussian kernel function combined with an optimal choice of parameters can produce high classification accuracy.

• Type: Conference
• Year: 2004
• Where: MICCAI
• Authors: Kashif Rajpoot, Nasir Rajpoot
• Keywords: Colon Tissue Cells, Gaussian Kernel Function, Medical Imaging, MICCAI 2004, SVM Classification Performance