Smart detection and diagnosis of diabetic retinopathy using bat based feature selection algorithm and deep forest technique

Author:

Modi Praveen,Kumar YugalORCID

Funder

Jaypee University of Information Technology

Publisher

Elsevier BV

Subject

General Engineering,General Computer Science

Reference50 articles.

1. Imagenet classification with deep convolutional neural networks;Krizhevsky;Communications of the ACM,2017

2. IDF diabetes atlas: Global estimates of the prevalence of diabetes for 2011 and 2030;Whiting;Diabetes Research and Clinical Practice,2011

3. Hajeb Mohammad Alipour, S., Rabbani, H., & Akhlaghi, M. R. (2012). Diabetic retinopathy grading by digital curvelet transform.Computational and mathematical methods in medicine,2012.

4. Automatic detection of diabetic retinopathy in non-dilated RGB retinal fundus images;SujithKumar;International Journal of Computer Applications,2012

5. Automatic recognition of severity level for diagnosis of diabetic retinopathy using deep visual features;Abbas;Medical & Biological Engineering & Computing,2017

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