Detection of Glaucoma on Fundus Images Using Deep Learning on a New Image Set Obtained with a Smartphone and Handheld Ophthalmoscope
Abstract
:1. Introduction
2. Related Work
2.1. Public Glaucoma Datasets
- RIM-ONE v1. The main objective of this study in 2011 was to provide a database of retinographies of 118 healthy subjects and 51 patients classified in various stages of glaucoma. Fundus images were acquired using a non-mydriatic Nidek AFC-210 camera with a Canon EOS 5D Mark II body with a field of view of 45°.
- RIM-ONE v2. It contains 255 images of healthy individuals and 200 images of patients with glaucoma. It is an extension of the first version and presents images manually segmented by a specialist doctor. Images were taken at HUC and HUMS using the same camera as in version 1.
- RIM-ONE v3. It contains 85 images of healthy individuals and 74 images of patients with glaucoma. Images were captured only at the HUC with a non-mydriatic Kowa WX 3D fundus camera with a full resolution of 2144 × 1424 pixels.
- G1020: G1020 images were collected at a private clinic in Kaiserslautern, Germany, between 2005 and 2017. Images were acquired with a 45° field of view using mydriasis. The dataset contains 1020 publicly available fundus images (724 healthy and 296 with glaucoma). Labeling of the images is provided, as well as segmentation of the optic disc and optic cup. In the final dataset, the images have sizes between 1944 × 2108 and 2426 × 3007 pixels [33].
2.2. Glaucoma Classification Algorithms in Fundus Images
3. Dataset Brazil Glaucoma (BrG)
3.1. Image Acquisition Process
3.2. Preprocessing of the Eye Fundus Images
3.3. Images with Noise
4. Model Selection and Training
4.1. Selection of CNN Models
4.2. Experimental Evaluation
4.3. Ensemble Construction and Results
5. Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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CNN | Input Size |
---|---|
Densenet [57] | 224 × 224 × 3–(RGB) |
Mobilenet [58] | 224 × 224 × 3–(RGB) |
InceptionV3 [59] | 299 × 299 × 3–(RGB) |
InceptionResnet [60] | 299 × 299 × 3–(RGB) |
Resnet50v2 [61] | 224 × 224 × 3–(RGB) |
Resnet101 [62] | 224 × 224 × 3–(RGB) |
Xception [63] | 299 × 299 × 3–(RGB) |
CNN-Individuals | AC | SE | SP | Pr | F1 | AUC | K |
---|---|---|---|---|---|---|---|
Densenet | 0.870 | 0.933 | 0.807 | 0.828 | 0.877 | 0.954 | 0.740 |
Mobilenet | 0.836 | 0.957 | 0.718 | 0.771 | 0.854 | 0.947 | 0.676 |
Inception-v3 | 0.835 | 0.913 | 0.757 | 0.789 | 0.847 | 0.930 | 0.670 |
InceptionResnet | 0.778 | 0.953 | 0.600 | 0.706 | 0.811 | 0.932 | 0.557 |
Resnet50v2 | 0.881 | 0.953 | 0.810 | 0.833 | 0.889 | 0.956 | 0.763 |
Resnet101 | 0.880 | 0.910 | 0.850 | 0.858 | 0.883 | 0.949 | 0.760 |
Xception | 0.806 | 0.926 | 0.686 | 0.747 | 0.827 | 0.919 | 0.613 |
CNN | Combinations | |||||
---|---|---|---|---|---|---|
1 | 2 | 3 | 4 | 5 | 6 | |
Resnet50v2 | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
Mobilenet | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
Densenet | ✓ | ✓ | ✓ | ✓ | ✓ | |
InceptionV3 | ✓ | ✓ | ✓ | ✓ | ||
Resnet101 | ✓ | ✓ | ✓ | |||
Inc-Resnet | ✓ | ✓ | ||||
Xception | ✓ |
Combinations | AC | SE | SP | Pr | F1 | AUC | K |
---|---|---|---|---|---|---|---|
1 | 0.861 | 0.767 | 0.957 | 0.946 | 0.847 | 0.967 | 0.723 |
2 | 0.865 | 0.763 | 0.966 | 0.858 | 0.849 | 0.968 | 0.730 |
3 | 0.905 | 0.850 | 0.960 | 0.955 | 0.899 | 0.965 | 0.810 |
4 | 0.865 | 0.770 | 0.960 | 0.950 | 0.850 | 0.964 | 0.730 |
5 | 0.838 | 0.703 | 0.973 | 0.963 | 0.813 | 0.963 | 0.677 |
6 | 0.853 | 0.730 | 0.976 | 0.969 | 0.832 | 0.961 | 0.700 |
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Bragança, C.P.; Torres, J.M.; Soares, C.P.d.A.; Macedo, L.O. Detection of Glaucoma on Fundus Images Using Deep Learning on a New Image Set Obtained with a Smartphone and Handheld Ophthalmoscope. Healthcare 2022, 10, 2345. https://doi.org/10.3390/healthcare10122345
Bragança CP, Torres JM, Soares CPdA, Macedo LO. Detection of Glaucoma on Fundus Images Using Deep Learning on a New Image Set Obtained with a Smartphone and Handheld Ophthalmoscope. Healthcare. 2022; 10(12):2345. https://doi.org/10.3390/healthcare10122345
Chicago/Turabian StyleBragança, Clerimar Paulo, José Manuel Torres, Christophe Pinto de Almeida Soares, and Luciano Oliveira Macedo. 2022. "Detection of Glaucoma on Fundus Images Using Deep Learning on a New Image Set Obtained with a Smartphone and Handheld Ophthalmoscope" Healthcare 10, no. 12: 2345. https://doi.org/10.3390/healthcare10122345