Abstract
Diabetic Retinopathy is the leading cause of blindness in the working-age population of the world. The main aim of this paper is to improve the accuracy of Diabetic Retinopathy detection by implementing a shadow removal and color correction step as a preprocessing stage from eye fundus images. For this, we rely on recent findings indicating that application of image dehazing on the inverted intensity domain amounts to illumination compensation. Inspired by this work, we propose a Shadow Removal Layer that allows us to learn the pre-processing function for a particular task. We show that learning the pre-processing function improves the performance of the network on the Diabetic Retinopathy detection task.
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Acknowledgments
This work is financed by the ERDF - European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme, by National Funds through the FCT - Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) within project CMUP-ERI/TIC/0028/2014.
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Smailagic, A., Sharan, A., Costa, P., Galdran, A., Gaudio, A., Campilho, A. (2019). Learned Pre-processing for Automatic Diabetic Retinopathy Detection on Eye Fundus Images. In: Karray, F., Campilho, A., Yu, A. (eds) Image Analysis and Recognition. ICIAR 2019. Lecture Notes in Computer Science(), vol 11663. Springer, Cham. https://doi.org/10.1007/978-3-030-27272-2_32
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DOI: https://doi.org/10.1007/978-3-030-27272-2_32
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