Attention Mechanism-Based Glaucoma Classification Model Using Retinal Fundus Images
Abstract
:1. Introduction
2. Materials and Methods
2.1. Dataset
2.2. Process of Data Collection and Preprocessing of Fundus Structures
2.3. Glaucoma Classification Model
3. Results
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Type of Disease | Number of Train Sets |
---|---|
Normal | 1135 |
Glaucoma | 207 |
Model | Sensitivity | Precision | F1-Score | |
---|---|---|---|---|
Glaucoma | VGG-16 [7] | 0.95 | 0.92 | 0.93 |
Transfer Learning + CNN [8] | 0.85 | 0.94 | 0.89 | |
R-CNN + LSTM [9] | 0.82 | 0.94 | 0.88 | |
AlexNet + ReliefF + XgBoost [10] | 0.98 | - | 0.98 | |
Our Model | 0.97 | 1.00 | 0.99 | |
Normal | VGG-16 [7] | 0.94 | 0.93 | 0.93 |
Transfer Learning + CNN [8] | 0.96 | 0.86 | 0.91 | |
R-CNN + LSTM [9] | 0.97 | 0.84 | 0.90 | |
AlexNet + ReliefF + XgBoost [10] | 0.98 | - | 0.98 | |
Our Model | 0.86 | 0.81 | 0.83 |
Model | Sensitivity | Precision | F1-Score | |
---|---|---|---|---|
Glaucoma | Our model w/o Attention. | 0.77 | 0.83 | 0.80 |
Our Model | 0.97 | 1.00 | 0.99 | |
Normal | Our model w/o Attention. | 0.82 | 0.75 | 0.78 |
Our Model | 0.86 | 0.81 | 0.83 |
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Cho, Y.-S.; Song, H.-J.; Han, J.-H.; Kim, Y.-S. Attention Mechanism-Based Glaucoma Classification Model Using Retinal Fundus Images. Sensors 2024, 24, 4684. https://doi.org/10.3390/s24144684
Cho Y-S, Song H-J, Han J-H, Kim Y-S. Attention Mechanism-Based Glaucoma Classification Model Using Retinal Fundus Images. Sensors. 2024; 24(14):4684. https://doi.org/10.3390/s24144684
Chicago/Turabian StyleCho, You-Sang, Ho-Jung Song, Ju-Hyuck Han, and Yong-Suk Kim. 2024. "Attention Mechanism-Based Glaucoma Classification Model Using Retinal Fundus Images" Sensors 24, no. 14: 4684. https://doi.org/10.3390/s24144684