Abstract
Diabetic Retinopathy is a Diabetes complication and the leading cause of blindness in the United States. Early detection can be accomplished by analysis of images of the retina, generally obtained by expensive fundus cameras. Recent developments allow the use of mobile ophthalmoscopes that can be adapted to smartphones to acquire these images, but the low computational power of smartphones limits the use of Computer-Aided Diagnosis systems. In this paper, an approach for automatic retinal image analysis on a smartphone is proposed, with emphasis on high sensitivity and fast computation. A set of 1200 images from the Messidor database were analyzed for extraction of features related to vessel segmentation, presence of exudates and microaneurysms. SVM and k-NN classifier models were trained with these features, resulting in a sensitivity of 87% and a specificity of 66%. An analysis of the computational performance validates the feasibility of using this approach on quad-core smartphones.
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Acknowledgements
We would like to acknowledge the financial support obtained from North Portugal Regional Operational Programme (NORTE 2020), Portugal 2020 and the European Regional Development Fund (ERDF) from European Union through the project Symbiotic technology for societal efficiency gains: Deus ex Machina (DEM), NORTE-01-0145-FEDER-000026.
The experimental data were kindly provided by the Messidor program partners (see http://www.adcis.net/en/DownloadThirdParty/Messidor.html).
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© 2016 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Costa, J., Sousa, I., Soares, F. (2016). Smartphone-Based Decision Support System for Elimination of Pathology-Free Images in Diabetic Retinopathy Screening. In: Ahmed, M., Begum, S., Raad, W. (eds) Internet of Things Technologies for HealthCare. HealthyIoT 2016. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 187. Springer, Cham. https://doi.org/10.1007/978-3-319-51234-1_13
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DOI: https://doi.org/10.1007/978-3-319-51234-1_13
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