Comparative Exudate Classification Using Support Vector Machines and Neural Networks

After segmenting candidate exudates regions in colour retinal images we present and compare two methods for their classification. The Neural Network based approach performs marginally better than the Support Vector Machine based approach, but we show that the latter are more flexible given criteria such as control of sensitivity and specificity rates. We present classification results for different learning algorithms for the Neural Net and use both hard and soft margins for the Support Vector Machines. We also present ROC curves to examine the trade-off between the sensitivity and specificity of the classifiers.

• Type: Conference
• Year: 2002
• Where: MICCAI
• Authors: Alireza Osareh, Majid Mirmehdi, Barry T. Thomas, Richard Markham
• Keywords: Candidate Exudates Regions, Medical Imaging, MICCAI 2002, Specificity Rates, Support Vector Machines