Title: Comparison of Classifiers for Retinal Pathology Images using SURF and Bag-of-Words Model

Year of Publication: Nov - 2014
Page Numbers: 72-78
Authors: Fanji Ari Mukti , Chikannan Eswaran, Noramiza Hashim
Conference Name: The International Conference on Artificial Intelligence and Pattern Recognition (AIPR2014)
- Malaysia


In this work, we compare the performances of four different classifiers for detection of diabetic retinopathy (DR) in retinal pathology images. The classifiers considered are Naïve Bayes Classifier, Decision Tree Classifier, Support Vector Machine (SVM) Classifier, and Feed-forward Neural Network Classifier. In the first step, the interest points and descriptors are extracted from the fundus images using the SURF algorithm. In the second step, based on the extracted interest points, a Bag-of-Visual-Words histogram is created for each image. Finally in the third step, the classifiers are first trained using the histograms of the images and then they are employed to classify whether a retinal image is normal or abnormal based on DR anomalies. For conducting the experiments, two well-known databases, namely, RetiDB and Messidor are used.