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Comparison of classifier performance for information fusion in automated Diabetic Retinopathy screening

Comparison of classifier performance for information fusion in automated Diabetic Retinopathy screening,10.1109/ISBI.2011.5872499,Meindert Niemeijer,M

Comparison of classifier performance for information fusion in automated Diabetic Retinopathy screening  
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Diabetic Retinopathy (DR) is a vascular disorder affecting the retina due to prolonged Diabetes. It can lead to sudden vision loss in advanced stages. Screening and routine monitoring is the most effective way of avoiding vision loss due to DR. Abramoff et al.[1] developed and evaluated an automated DR screening system. One of the most important parts of this system, the information fusion module, combines information obtained from different images and various image properties. Niemeijer et al. [2] compared several methods for DR information fusion and concluded that k-Nearest Neighbour (kNN) provided the best performance for their system. The aim of this work was to compare performance of the Random Forest (RF) classifier with that of the kNN classifier for DR information fusion. We performed experiments on a dataset containing images from 10303 eye examinations. Additionally we also compared performance of the two classifiers in an important sub-problem of DR screening - red lesion detection. In both the experiments, the RF classifier showed significantly better performance.
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