تقرير
Direct Classification of Type 2 Diabetes From Retinal Fundus Images in a Population-based Sample From The Maastricht Study
| العنوان: | Direct Classification of Type 2 Diabetes From Retinal Fundus Images in a Population-based Sample From The Maastricht Study |
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| المؤلفون: | Heslinga, Friso G., Pluim, Josien P. W., Houben, A. J. H. M., Schram, Miranda T., Henry, Ronald M. A., Stehouwer, Coen D. A., van Greevenbroek, Marleen J., Berendschot, Tos T. J. M., Veta, Mitko |
| سنة النشر: | 2019 |
| المجموعة: | Computer Science Statistics |
| مصطلحات موضوعية: | Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Statistics - Machine Learning |
| الوصف: | Type 2 Diabetes (T2D) is a chronic metabolic disorder that can lead to blindness and cardiovascular disease. Information about early stage T2D might be present in retinal fundus images, but to what extent these images can be used for a screening setting is still unknown. In this study, deep neural networks were employed to differentiate between fundus images from individuals with and without T2D. We investigated three methods to achieve high classification performance, measured by the area under the receiver operating curve (ROC-AUC). A multi-target learning approach to simultaneously output retinal biomarkers as well as T2D works best (AUC = 0.746 [$\pm$0.001]). Furthermore, the classification performance can be improved when images with high prediction uncertainty are referred to a specialist. We also show that the combination of images of the left and right eye per individual can further improve the classification performance (AUC = 0.758 [$\pm$0.003]), using a simple averaging approach. The results are promising, suggesting the feasibility of screening for T2D from retinal fundus images. Comment: to be published in the proceeding of SPIE - Medical Imaging 2020, 6 pages, 1 figure |
| نوع الوثيقة: | Working Paper |
| الوصول الحر: | http://arxiv.org/abs/1911.10022Test |
| رقم الانضمام: | edsarx.1911.10022 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |