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. 2019 Dec;27(5):327-332.
doi: 10.5455/aim.2019.27.327-332.

Deep Transfer Learning Models for Medical Diabetic Retinopathy Detection

Nour Eldeen M Khalifa  1 , Mohamed Loey  2 , Mohamed Hamed N Taha  1 , Hamed Nasr Eldin T Mohamed  3
Affiliations

Deep Transfer Learning Models for Medical Diabetic Retinopathy Detection

Nour Eldeen M Khalifa et al. Acta Inform Med. 2019 Dec.

Abstract

Introduction: Diabetic retinopathy (DR) is the most common diabetic eye disease worldwide and a leading cause of blindness. The number of diabetic patients will increase to 552 million by 2034, as per the International Diabetes Federation (IDF).

Aim: With advances in computer science techniques, such as artificial intelligence (AI) and deep learning (DL), opportunities for the detection of DR at the early stages have increased. This increase means that the chances of recovery will increase and the possibility of vision loss in patients will be reduced in the future.

Methods: In this paper, deep transfer learning models for medical DR detection were investigated. The DL models were trained and tested over the Asia Pacific Tele-Ophthalmology Society (APTOS) 2019 dataset. According to literature surveys, this research is considered one the first studies to use of the APTOS 2019 dataset, as it was freshly published in the second quarter of 2019. The selected deep transfer models in this research were AlexNet, Res-Net18, SqueezeNet, GoogleNet, VGG16, and VGG19. These models were selected, as they consist of a small number of layers when compared to larger models, such as DenseNet and InceptionResNet. Data augmentation techniques were used to render the models more robust and to overcome the overfitting problem.

Results: The testing accuracy and performance metrics, such as the precision, recall, and F1 score, were calculated to prove the robustness of the selected models. The AlexNet model achieved the highest testing accuracy at 97.9%. In addition, the achieved performance metrics strengthened our achieved results. Moreover, AlexNet has a minimum number of layers, which decreases the training time and the computational complexity.

Keywords: Convolutional Neural Network; Deep Transfer Learning; Diabetic Retinopathy; Machine Learning.

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Conflict of interest statement

There are no conflicts of interest.

Figures

Figure 1.
Figure 1.. Sample image for each class in the APTOS 2019 dataset
Figure 2.
Figure 2.. Proposed model’s customization for medical diabetic retinopathy detection
Figure 3.
Figure 3.. (a) AlexNet and (b) VGG16 confusion matrices
Figure 4.
Figure 4.. (a) ResNet18 and (b) SqueezeNet confusion matrices
Figure 5.
Figure 5.. (a) VGG19, and (b) GoogleNet confusion matrices
See this image and copyright information in PMC

References

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