# A systematic review of transfer learning based approaches for diabetic retinopathy detection

arXiv: https://arxiv.org/abs/2105.13793 · HTML: https://mlsanity.com/paper/2105.13793/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 75 references that are in the map.
- First posted on arXiv 28 May 2021.

## Builds on (most cited first, 6 of 6)

- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Post-mortem Iris Recognition with Deep-Learning-based Image Segmentation](https://mlsanity.com/paper/1901.01708/) (2019)
- [Deep Learning based Computer-Aided Diagnosis Systems for Diabetic Retinopathy: A Survey](https://mlsanity.com/paper/1811.01238/) (2018)
- [Replication study: Development and validation of deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs](https://mlsanity.com/paper/1803.04337/) (2018)
- [Blended Multi-Modal Deep ConvNet Features for Diabetic Retinopathy Severity Prediction](https://mlsanity.com/paper/2006.00197/) (2020)
- [Transfer Learning based Detection of Diabetic Retinopathy from Small Dataset](https://mlsanity.com/paper/1905.07203/) (2019)

## Built on by (most cited first, 1 of 1)

- [Addressing High Class Imbalance in Multi-Class Diabetic Retinopathy Severity Grading with Augmentation and Transfer Learning](https://mlsanity.com/paper/2507.17121/) (2025)

## Abstract (arXiv)

Cases of diabetes and related diabetic retinopathy (DR) have been increasing at an alarming rate in modern times. Early detection of DR is an important problem since it may cause permanent blindness in the late stages. In the last two decades, many different approaches have been applied in DR detection. Reviewing academic literature shows that deep neural networks (DNNs) have become the most preferred approach for DR detection. Among these DNN approaches, Convolutional Neural Network (CNN) models are the most used ones in the field of medical image classification. Designing a new CNN architecture is a tedious and time-consuming approach. Additionally, training an enormous number of parameters is also a difficult task. Due to this reason, instead of training CNNs from scratch, using pre-trained models has been suggested in recent years as transfer learning approach. Accordingly, the present study as a review focuses on DNN and Transfer Learning based applications of DR detection considering 38 publications between 2015 and 2020. The published papers are summarized using 9 figures and 10 tables, giving information about 22 pre-trained CNN models, 12 DR data sets and standard performance metrics.