An Ensemble-Based System for Microaneurysm Detection and Diabetic Retinopathy Grading

By Bálint Antal, András Hajdu. IEEE Transactions on Biomedical Engineering, 2012

375 citations recorded by the source corpus.

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 (DR) grading, we also tested the proposed method for this task on the publicly available Messidor database, where a promising AUC 0.90 ± 0.01 is achieved in a “DR/non-DR”-type classification based on the presence or absence of the microaneurysms.

Subfields: Ophthalmology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/c17ddc3767fa3c10eeb1d1c6b0aa88dfdc18095c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Ophthalmology

1. Carson K. Lam
2. Margaret Guo
3. Tony Lindsey
4. Darvin Yi

In Radiology, Nuclear Medicine and Imaging

1. Carson K. Lam
2. Margaret Guo
3. Tony Lindsey
4. Darvin Yi

Citations in the corpus

• Fundus Image Classification Using VGG-19 Architecture with PCA and SVD (2018, neutral): “Antal et al. [43”
• Automated Detection of Diabetic Retinopathy using Deep Learning (2018, methodology): “Additional methods of detecting microaneurysms and grading DR involving k-NN 520 , support vector machines 22 , and ensemble-based methods 6 have yielded sensitivities and specificities within the 90% range using various feature extraction techniques and preprocessing algorithms.”
• Uniqueness-Driven Saliency Analysis for Automated Lesion Detection with Applications to Retinal Diseases (2018, neutral): “A final score (FS) was computed by averaging the sensitivity values obtained at each of these seven predefined FPIs [20].”
• Convolutional neural networks based transfer learning for diabetic retinopathy fundus image classification (2017, neutral): “[3] 88.”