An Ensemble-based System for Microaneurysm Detection and Diabetic Retinopathy Grading
This work addresses the challenge of reliable microaneurysm detection for diabetic retinopathy grading in medical imaging, showing incremental improvements over existing methods.
The paper tackled the problem of microaneurysm detection in digital fundus images by proposing an ensemble-based framework that combines internal components like preprocessing methods and candidate extractors, achieving first rank in an online competition and an AUC of 0.90 with 0.01 uncertainty for diabetic retinopathy grading on the Messidor database.
Reliable microaneurysm detection in digital fundus images is still an open issue in medical image processing. We propose an ensemble-based framework to improve microaneurysm detection. Unlike the well-known approach of considering the output of multiple classifiers, we propose a combination of internal components of microaneurysm detectors, namely preprocessing methods and candidate extractors. We have evaluated our approach for microaneurysm detection in an online competition, where this algorithm is currently ranked as first and also on two other databases. Since microaneurysm detection is decisive in diabetic retinopathy grading, we also tested the proposed method for this task on the publicly available Messidor database, where a promising AUC 0.90 with 0.01 uncertainty is achieved in a 'DR/non-DR'-type classification based on the presence or absence of the microaneurysms.