A Tailored Complex Medical Decision Analysis Model for Diabetic Retinopathy Classification Based on Optimized Un-Supervised Feature Learning Approach
Author:
Publisher
Springer Science and Business Media LLC
Subject
Multidisciplinary
Link
https://link.springer.com/content/pdf/10.1007/s13369-022-07057-0.pdf
Reference54 articles.
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2. Porwal, P.; Pachade, S.; Kamble, R.; Kokare, M.; Deshmukh, G.; Sahasrabuddhe, V.; Meriaudeau, F.: Indian diabetic retinopathy image dataset (IDRiD): a database for diabetic retinopathy screening research. Data 3(3), 25 (2018). https://doi.org/10.3390/data3030025
3. Rajalakshmi, R.; Subashini, R.; Anjana, R.M.; Mohan, V.: Automated diabetic retinopathy detection in smartphone-based fundus photography using artificial intelligence. Eye 32(6), 1138–1144 (2018). https://doi.org/10.1038/s41433-018-0064-9
4. Altmann, C.; Schmidt, M.H.: The role of microglia in diabetic retinopathy: inflammation, microvasculature defects and neurodegeneration. Int. J. Mol. Sci. 19(1), 110 (2018). https://doi.org/10.3390/ijms19010110
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