Automated Diabetic Retinopathy detection and classification using stochastic coordinate descent deep learning architectures
Author:
Publisher
Elsevier BV
Subject
General Medicine
Reference14 articles.
1. Deep learning architecture based on segmented fundus image features for classification of diabetic retinopathy;Das;Biomed. Signal Process. Control,2021
2. R.E. Hacisoftaoglu M. Karakaya A.B. Sallam Deep learning frameworks for diabetic retinopathy detection with smartphone-based retinal imaging systems Pattern Recognition Letters 135 2020 409 417
3. F.F. Wahid G. Raju Diabetic Retinopathy Detection Using Convolutional Neural Network—A Study Data Science and Security 2020 127 133
4. Alyoubi, W. L., Shalash, W. M., & Abulkhair, M. F. (2020). Diabetic retinopathy detection through deep learning techniques: A review. Informatics in Medicine Unlocked, 100377.
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1. Enhancing Diabetic Retinopathy Detection with an Innovative ACSE-CLF Algorithm: A Deep Learning Approach;2023 7th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC);2023-10-11
2. Automated Detection of Diabetic Retinopathy from the Fundus Photography using Deep Learning Method;International Journal of Advanced Research in Science, Communication and Technology;2023-04-20
3. Deep Learning for Diabetic Retinopathy Analysis: A Review, Research Challenges, and Future Directions;Sensors;2022-09-08
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