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- W1530546985 abstract "In this paper, we present an approach to improve microaneurysm detection in color fundus images. This task is usually realized by candidate extraction, which is followed by a classification step. The proposed method aims to increase the number of true positives in the first phase of the microaneurysm detection process. Thus, we establish a framework for selecting an optimal combination of preprocessing methods and candidate extractors. Our investigation shows that the state-of-the-art candidate extractors provide significantly improved results, when they are optimally combined with preprocessing approaches. We show that this performance can be further increased with an ensemble formed by a globally optimal combination of the preprocessing methods and candidate extractors." @default.
- W1530546985 created "2016-06-24" @default.
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- W1530546985 date "2010-08-23" @default.
- W1530546985 modified "2023-09-28" @default.
- W1530546985 title "Improving microaneurysm detection in color fundus images by using an optimal combination of preprocessing methods and candidate extractors" @default.
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- W1530546985 doi "https://doi.org/10.5281/zenodo.42119" @default.
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