Optimizing Glaucoma Diagnosis with Deep Learning-Based Segmentation and Classification of Retinal Images
Abstract
:1. Introduction
- We proposed an integrated model that combines U-Net with ResNet34 for robust segmentation and EfficientNet-B0 for precise classification. The key innovation in our study lies in the integration of U-Net with ResNet34 to enhance segmentation accuracy and the use of EfficientNet-B0 for precise classification, a novel application specifically tailored for glaucoma detection. While U-Net has been widely used for medical image segmentation and combined with various backbones in other domains such as tumor detection and organ segmentation, our approach uniquely addresses the specific challenges of glaucoma detection with this combination.
- We addressed the challenge of training CNNs from scratch with limited data and hardware constraints by adopting transfer learning like ImageNet and data augmentation techniques on enhancing model performance in glaucoma detection.
- Our model shows remarkable performance across ORIGA, RIM-ONE DL, and HRF datasets in terms of accuracy, AUC, precision, recall, F1 score, specificity, and (mIoU).
- Our model demonstrates superior performance compared to the state of the art in detecting glaucoma across various datasets.
- The implemented method enabled efficient network training, reduced computational time, and enhanced model accuracy, promoting medical field applications and clinical practices for early glaucoma detection.
2. Literature Review
3. Materials and Methods
3.1. Datasets Description
3.2. Proposed Methodology
3.2.1. Data Preprocessing
3.2.2. Segmentation
- Extraction of ROI
- Postprocessing of Segmented Images
3.2.3. Classification
3.2.4. Evaluation
3.2.5. Experimental Design
- Hyperparameter Selection
4. Results and Discussion
4.1. Data Preprocessing
4.2. U-Net with Pretrained Backbones for Segmentation
4.3. Pretrained Models for Classification
5. Conclusions and Future Work
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Sudhan, M.B.; Sinthuja, M.; Pravinth Raja, S.; Amutharaj, J.; Charlyn Pushpa Latha, G.; Sheeba Rachel, S.; Anitha, T.; Rajendran, T.; Waji, Y.A. Segmentation and Classification of Glaucoma Using U-Net with Deep Learning Model. J. Healthc. Eng. 2022, 2022, 1601354. [Google Scholar] [CrossRef] [PubMed]
- Nazir, T.; Irtaza, A.; Starovoitov, V. Optic Disc and Optic Cup Segmentation for Glaucoma Detection from Blur Retinal Images Using Improved Mask-RCNN. Int. J. Opt. 2021, 2021, 6641980. [Google Scholar] [CrossRef]
- Latif, J.; Tu, S.; Xiao, C.; Ur Rehman, S.; Imran, A.; Latif, Y. ODGNet: A Deep Learning Model for Automated Optic Disc Localization and Glaucoma Classification Using Fundus Images. SN Appl. Sci. 2022, 4, 98. [Google Scholar] [CrossRef]
- Veena, H.N.; Muruganandham, A.; Senthil Kumaran, T. A Novel Optic Disc and Optic Cup Segmentation Technique to Diagnose Glaucoma Using Deep Learning Convolutional Neural Network over Retinal Fundus Images. J. King Saud. Univ. Comput. Inf. Sci. 2022, 34, 6187–6198. [Google Scholar] [CrossRef]
- Camara, J.; Neto, A.; Pires, I.M.; Villasana, M.V.; Zdravevski, E.; Cunha, A. Literature Review on Artificial Intelligence Methods for Glaucoma Screening, Segmentation, and Classification. J. Imaging 2022, 8, 19. [Google Scholar] [CrossRef] [PubMed]
- Hesamian, M.H.; Jia, W.; He, X.; Kennedy, P. Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges. J. Digit. Imaging 2019, 32, 582–596. [Google Scholar] [CrossRef] [PubMed]
- Kim, M.; Han, J.C.; Hyun, S.H.; Janssens, O.; Van Hoecke, S.; Kee, C.; De Neve, W. Medinoid: Computer-Aided Diagnosis and Localization of Glaucoma Using Deep Learning †. Appl. Sci. 2019, 9, 3064. [Google Scholar] [CrossRef]
- Norouzifard, M.; Nemati, A.; GholamHosseini, H.; Klette, R.; Nouri-Mahdavi, K.; Yousefi, S. Automated Glaucoma Diagnosis Using Deep and Transfer Learning: Proposal of a System for Clinical Testing. In Proceedings of the 2018 International Conference on Image and Vision Computing (IVCNZ), Auckland, New Zealand, 19–21 November 2018; IEEE: Piscataway, NJ, USA, 2018; pp. 1–6. [Google Scholar]
- Nawaz, M.; Nazir, T.; Javed, A.; Tariq, U.; Yong, H.-S.; Khan, M.A.; Cha, J. An Efficient Deep Learning Approach to Automatic Glaucoma Detection Using Optic Disc and Optic Cup Localization. Sensors 2022, 22, 434. [Google Scholar] [CrossRef] [PubMed]
- Maheshwari, S.; Kanhangad, V.; Pachori, R.B. CNN-Based Approach for Glaucoma Diagnosis Using Transfer Learning and LBP-Based Data Augmentation. arXiv 2020, arXiv:2002.08013. [Google Scholar]
- Mallick, S.; Saha, N.; Paul, J.; Ganguli, I.; Debnath, S.; Sil, J. An Efficient Deep Learning Framework for Glaucoma Diagnosis Using Convolution Mixed Transformer Network. In Proceedings of the TENCON 2023—2023 IEEE Region 10 Conference (TENCON), Chiang Mai, Thailand, 31 October–3 November 2023; IEEE: Piscataway, NJ, USA, 2023; pp. 1111–1116. [Google Scholar]
- Manassakorn, A.; Auethavekiat, S.; Sa-Ing, V.; Chansangpetch, S.; Ratanawongphaibul, K.; Uramphorn, N.; Tantisevi, V. GlauNet: Glaucoma Diagnosis for OCTA Imaging Using a New CNN Architecture. IEEE Access 2022, 10, 95613–95622. [Google Scholar] [CrossRef]
- Yi, S.; Zhou, L.; Ma, L.; Shao, D. MTRA-CNN: A Multi-Scale Transfer Learning Framework for Glaucoma Classification in Retinal Fundus Images. IEEE Access 2023, 11, 142689–142701. [Google Scholar] [CrossRef]
- Virbukaitė, S.; Bernatavičienė, J.; Imbrasienė, D. Glaucoma Identification Using Convolutional Neural Networks Ensemble for Optic Disc and Cup Segmentation. IEEE Access 2024, 12, 82720–82729. [Google Scholar] [CrossRef]
- Shyamalee, T.; Meedeniya, D.; Lim, G.; Karunarathne, M. Automated Tool Support for Glaucoma Identification with Explainability Using Fundus Images. IEEE Access 2024, 12, 17290–17307. [Google Scholar] [CrossRef]
- Zhang, Z.; Yin, F.S.; Liu, J.; Wong, W.K.; Tan, N.M.; Lee, B.H.; Cheng, J.; Wong, T.Y. ORIGA-light: An Online Retinal Fundus Image Database for Glaucoma Analysis and Research. In Proceedings of the 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, Buenos Aires, Argentina, 31 August–4 September 2010; IEEE: Piscataway, NJ, USA, 2010; pp. 3065–3068. [Google Scholar]
- Budai, A.; Bock, R.; Maier, A.; Hornegger, J.; Michelson, G. Robust Vessel Segmentation in Fundus Images. Int. J. Biomed. Imaging 2013, 2013, 154860. [Google Scholar] [CrossRef] [PubMed]
- Fumero Batista, F.J.; Diaz-Aleman, T.; Sigut, J.; Alayon, S.; Arnay, R.; Angel-Pereira, D. RIM-ONE DL: A Unified Retinal Image Database for Assessing Glaucoma Using Deep Learning. Image Anal. Stereol. 2020, 39, 161–167. [Google Scholar] [CrossRef]
- Orlando, J.I.; Fu, H.; Barbosa Breda, J.; van Keer, K.; Bathula, D.R.; Diaz-Pinto, A.; Fang, R.; Heng, P.-A.; Kim, J.; Lee, J.; et al. REFUGE Challenge: A Unified Framework for Evaluating Automated Methods for Glaucoma Assessment from Fundus Photographs. Med. Image Anal. 2020, 59, 101570. [Google Scholar] [CrossRef] [PubMed]
- Juneja, M.; Thakur, N.; Thakur, S.; Uniyal, A.; Wani, A.; Jindal, P. GC-NET for Classification of Glaucoma in the Retinal Fundus Image. Mach. Vis. Appl. 2020, 31, 38. [Google Scholar] [CrossRef]
- Castiglioni, I.; Rundo, L.; Codari, M.; Di Leo, G.; Salvatore, C.; Interlenghi, M.; Gallivanone, F.; Cozzi, A.; D’Amico, N.C.; Sardanelli, F. AI Applications to Medical Images: From Machine Learning to Deep Learning. Phys. Medica 2021, 83, 9–24. [Google Scholar] [CrossRef] [PubMed]
- Buslaev, A.; Iglovikov, V.I.; Khvedchenya, E.; Parinov, A.; Druzhinin, M.; Kalinin, A.A. Albumentations: Fast and Flexible Image Augmentations. Information 2020, 11, 125. [Google Scholar] [CrossRef]
- Segmentation Models with Pretrained Backbones. Keras and TensorFlow Keras. Available online: https://github.com/qubvel/segmentation_models (accessed on 4 July 2024).
- Ansari, S. Building Computer Vision Applications Using Artificial Neural Networks; Apress: Berkeley, CA, USA, 2020; ISBN 978-1-4842-5886-6. [Google Scholar]
- Tan, M.; Le, Q.V. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. arXiv 2019, arXiv:1905.11946. [Google Scholar] [CrossRef]
- Goutte, C.; Gaussier, E. A Probabilistic Interpretation of Precision, Recall and F-Score, with Implication for Evaluation. In Proceedings of the European Conference on Information Retrieval, Santiago de Compostela, Spain, 21–23 March 2005; Springer: Berlin/Heidelberg, Germany, 2005; pp. 345–359. [Google Scholar]
- Bharati, S.; Khan, T.Z.; Podder, P.; Hung, N.Q. A Comparative Analysis of Image Denoising Problem: Noise Models, Denoising Filters and Applications. In Cognitive Internet of Medical Things for Smart Healthcare: Services and Applications; Springer: Berlin/Heidelberg, Germany, 2021; pp. 49–66. [Google Scholar]
- Colab Pro. Available online: https://colab.research.google.com/signup (accessed on 4 July 2024).
- Gradio. Available online: https://www.gradio.app/ (accessed on 6 May 2024).
Dataset | Segmentation/Classification | Number of Images | Total | |
---|---|---|---|---|
Glaucoma | Normal | |||
ORIGA [16] | Both | 168 | 482 | 650 |
HRF [17] | Classification | 15 | 15 | 30 |
RIM-ONE DL [18] | Both | 172 | 313 | 485 |
REFUGE [19] | Segmentation | 40 | 360 | 400 |
Dataset | No. of Images/Masks | Balanced Dataset | Total | ||
---|---|---|---|---|---|
Glaucoma | Normal | Glaucoma | Normal | ||
ORIGA and REFUGE-TS | 208 | 842 | 208 | 208 | 416 |
ORIGA and RIM-ONE DL | 340 | 795 | 340 | 340 | 680 |
Stage | Dataset | Total Numbers of Data | Augmentation Technique | |
---|---|---|---|---|
Before Augmentation | After Augmentation | |||
Segmentation | ORIGA and REFUGE-TS | 416 | 832 | RR, RB, CA, RHF |
1248 | RR, RHF | |||
ORIGA and RIM-ONE DL | 680 | 1360 | RR, RB, CA, RHF | |
2040 | RR, RHF |
Dataset | Number of Images | Balanced Dataset | Total | ||
---|---|---|---|---|---|
Glaucoma | Normal | Glaucoma | Normal | ||
ORIGA [16] | 168 | 482 | 168 | 168 | 336 |
HRF [17] | 15 | 15 | 15 | 15 | 30 |
RIM-ONE DL [18] | 172 | 313 | 172 | 172 | 344 |
Stage | Dataset | Augmentation Technique | Total Count of Images after Augmentation | |
---|---|---|---|---|
Glaucoma | Normal | |||
Classification | ORIGA [16] | RR, BA, RHF | 1006 | 1006 |
HRF [17] | 87 | 87 | ||
RIM-ONE DL [18] | 959 | 959 |
Stage | Experiments No. | Objective |
---|---|---|
Segmentation | Experiment 1 | To identify the backbone architecture that yielded the highest performance for the U-Net model |
Experiment 2 | To evaluate the effect of augmentation techniques on image segmentation using ResNet34 in the U-Net model | |
Experiment 3 | To test batch sizes for mIoU impact | |
Experiment 4 | To use varied epochs | |
Classification | Experiment 5 | To test EfficientNetB0 on ORIGA, RIM-ONE DL, and HRF datasets with different batch sizes |
Experiment 6 | To evaluate the performance of model with different numbers of epochs | |
Experiment 7 | To assess the impact of data augmentation on image classification | |
Experiment 8 | To assess the impact of varying epochs on the performance of an EfficientNetB0 model trained on augmented ORIGA and RIM-ONE DL |
Parameter | Value |
---|---|
Pretrained | Yes (ImageNet) |
Optimizer | Adam |
Activation | SoftMax |
Learning rate | 0.0001 |
Proposed backbone | ResNet34 |
Batch sizes | 8, 16, 32, 64, 128 |
Epochs | 30, 40, 50 |
Alternative backbones | ResNet50, VGG19, DenseNet121, EfficientNetB0 |
Parameter | Value |
---|---|
Optimizer | Adam |
Activation | Sigmoid |
Learning rate | 0.0001 |
Loss function | Binary cross-entropy |
Proposed model | EfficientNetB0 |
Alternative models | DenseNet121, VGG19, ResNet50 |
Experiment 1.1 | |||||
---|---|---|---|---|---|
Dataset | Epoch | Batch Size | Model | Backbone | mIoU |
ORIGA and REFUGE-TS | 30 | 8 | U-Net | ResNet34 | 0.8432825 |
ResNet50 | 0.8392462 | ||||
VGG19 | 0.281714 | ||||
EfficientNetb0 | 0.5335277 | ||||
DenseNet121 | 0.6566112 |
Experiment 1.2 | |||||
---|---|---|---|---|---|
Dataset | Epoch | Batch Size | Model | Backbone | mIoU |
ORIGA and RIM-ONE DL | 30 | 8 | U-Net | ResNet34 | 0.82803315 |
ResNet50 | 0.82745105 | ||||
VGG19 | 0.2535684 | ||||
EfficientNetb0 | 0.27394077 | ||||
DenseNet121 | 0.28304914 |
Experiment 2.1 | |||||
---|---|---|---|---|---|
Dataset | Batch Size | Model | Augmentation Technique | Number of Images | mIoU |
ORIGA and REFUGE-TS | 8 | U-Net with ResNet34 | RR, RB, CA, RHF | 832 | 0.926651 |
RR, RHF | 832 | 0.929291 | |||
1248 | 0.948287 |
Experiment 2.2 | |||||
---|---|---|---|---|---|
Dataset | Batch Size | Model | Augmentation Technique | Number of Images | mIoU |
ORIGA and RIM-ONE DL | 8 | U-Net with ResNet34 | RR, RB, CA, RHF | 1360 | 0.967521 |
RR, RHF | 1360 | 0.970067 | |||
2040 | 0.977422 |
Experiment 3.1 | |||||
---|---|---|---|---|---|
Dataset | Epoch | Batch Size | Model | Backbone | mIoU |
ORIGA and REFUGE-TS | 30 | 8 | U-Net | ResNet34 | 0.94828725 |
16 | 0.9441021 | ||||
32 | 0.8463246 | ||||
64 | 0.6734596 | ||||
128 | 0.51045746 |
Experiment 3.2 | |||||
---|---|---|---|---|---|
Dataset | Epoch | Batch Size | Model | Backbone | mIoU |
ORIGA and RIM-ONE DL | 30 | 8 | U-Net | ResNet34 | 0.977422 |
16 | 0.976188 | ||||
32 | 0.9721312 | ||||
64 | 0.6423847 | ||||
128 | 0.5387978 |
Experiment 1.4 | |||||
---|---|---|---|---|---|
Dataset | Epoch | Batch Size | Model | Backbone | mIoU |
ORIGA and REFUGE-TS | 30 | 16 | U-Net | ResNet34 | 0.944102 |
40 | 0.950539 | ||||
50 | 0.953595 |
Experiment 2.4 | |||||
---|---|---|---|---|---|
Dataset | Epoch | Batch Size | Model | Backbone | mIoU |
ORIGA and RIM-ONE DL | 30 | 16 | U-Net | ResNet34 | 0.976188 |
40 | 0.9792807 | ||||
50 | 0.98035127 | ||||
60 | 0.9826489 |
Sub-Experiments (1.1, 2.1, 3.1, and 4.1) | Sub-Experiments (1.2, 2.2, 3.2, and 4.2) | |
---|---|---|
Datasets | ORIGA and REFUGE-TS | ORIGA and RIM-ONE DL |
Pretrained backbone | ResNet34 | ResNet34 |
Denoising | No enhancement observed | No enhancement observed |
Augmentation | Positive impact observed | Positive impact observed |
Epochs no. | 40 | 50 |
Highest mIoU | 0.950539 | 0.98035127 |
Performance Metrics | Pretrained Models | |||
---|---|---|---|---|
EfficientNetB0 | DenseNet121 | VGG19 | ResNet50 | |
Train accuracy | 0.97 | 0.88 | 0.8 | 0.59 |
Test accuracy | 0.9 | 0.82 | 0.88 | 0.63 |
AUC | 0.9 | 0.82 | 0.88 | 0.63 |
Precision | 0.9 | 0.87 | 0.89 | 0.63 |
Recall | 0.9 | 0.82 | 0.88 | 0.63 |
F1 score | 0.9 | 0.82 | 0.88 | 0.63 |
Specificity | 0.92 | 1 | 0.96 | 0.62 |
Performance Metrics | Pretrained Models | |||
---|---|---|---|---|
EfficientNetB0 | DenseNet121 | VGG19 | ResNet50 | |
Train accuracy | 0.98 | 0.97 | 0.92 | 0.51 |
Test accuracy | 0.92 | 0.92 | 0.81 | 0.50 |
AUC | 0.92 | 0.92 | 0.81 | 0.50 |
Precision | 0.93 | 0.93 | 0.86 | 0.25 |
Recall | 0.92 | 0.92 | 0.81 | 0.50 |
F1 score | 0.92 | 0.92 | 0.80 | 0.33 |
Specificity | 0.88 | 0.88 | 1.0 | 0.0 |
Performance Metrics | Pretrained Models | |||
---|---|---|---|---|
EfficientNetB0 | DenseNet121 | VGG19 | ResNet50 | |
Train accuracy | 0.98 | 0.97 | 0.90 | 0.59 |
Test accuracy | 1.00 | 0.62 | 0.38 | 0.50 |
AUC | 1.00 | 0.63 | 0.38 | 0.50 |
Precision | 1.00 | 0.62 | 0.37 | 0.50 |
Recall | 1.00 | 0.62 | 0.38 | 0.50 |
F1 score | 1.00 | 0.56 | 0.37 | 0.50 |
Specificity | 1.00 | 1.00 | 0.50 | 0.50 |
Pretrained EfficientNetB0 on ORIGA Dataset | |||||||
---|---|---|---|---|---|---|---|
Batch Size | Train Accuracy | Test Accuracy | AUC | Precision | Recall | F1 Score | Specificity |
8 | 0.97 | 0.90 | 0.90 | 0.90 | 0.90 | 0.90 | 0.92 |
16 | 0.98 | 0.49 | 0.50 | 0.25 | 0.50 | 0.33 | 0.0 |
32 | 0.99 | 0.53 | 0.54 | 0.76 | 0.54 | 0.41 | 0.08 |
64 | 0.99 | 0.51 | 0.52 | 0.75 | 0.52 | 0.37 | 0.04 |
Pretrained EfficientNetB0 on RIM-ONE DL Dataset | |||||||
---|---|---|---|---|---|---|---|
Batch Size | Train Accuracy | Test Accuracy | AUC | Precision | Recall | F1 Score | Specificity |
8 | 0.98 | 0.92 | 0.92 | 0.93 | 0.92 | 0.92 | 0.88 |
16 | 0.99 | 0.90 | 0.90 | 0.90 | 0.90 | 0.90 | 0.92 |
32 | 0.99 | 0.85 | 0.85 | 0.87 | 0.85 | 0.84 | 0.96 |
64 | 0.99 | 0.77 | 0.77 | 0.84 | 0.77 | 0.76 | 1.0 |
Pretrained EfficientNetB0 on HRF Dataset | |||||||
---|---|---|---|---|---|---|---|
Batch Size | Train Accuracy | Test Accuracy | AUC | Precision | Recall | F1 Score | Specificity |
4 | 0.98 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 |
8 | 0.98 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 |
16 | 1.00 | 0.50 | 0.50 | 0.50 | 0.50 | 0.50 | 0.50 |
32 | 1.00 | 0.75 | 0.75 | 0.75 | 0.75 | 0.75 | 0.75 |
Pretrained EfficientNetB0 on ORIGA Dataset | ||||||||
---|---|---|---|---|---|---|---|---|
Batch Size | Epoch | Train Accuracy | Test Accuracy | AUC | Precision | Recall | F1 Score | Specificity |
8 | 10 | 0.97 | 0.66 | 0.67 | 0.80 | 0.67 | 0.63 | 0.35 |
15 | 0.97 | 0.90 | 0.90 | 0.90 | 0.90 | 0.90 | 0.92 | |
20 | 1.0 | 0.92 | 0.92 | 0.93 | 0.92 | 0.92 | 1.0 | |
30 | 0.98 | 0.86 | 0.86 | 0.87 | 0.86 | 0.86 | 0.81 |
Pretrained EfficientNetB0 on RIM-ONE DL Dataset | ||||||||
---|---|---|---|---|---|---|---|---|
Batch Size | Epoch | Train Accuracy | Test Accuracy | AUC | Precision | Recall | F1 Score | Specificity |
8 | 7 | 0.96 | 0.60 | 0.60 | 0.78 | 0.60 | 0.52 | 0.19 |
11 | 0.97 | 0.84 | 0.85 | 0.85 | 0.85 | 0.85 | 0.77 | |
15 | 0.94 | 0.90 | 0.90 | 0.90 | 0.90 | 0.90 | 0.92 | |
20 | 0.96 | 0.85 | 0.85 | 0.85 | 0.85 | 0.85 | 0.81 |
Pretrained EfficientNetB0 on HRF Dataset | ||||||||
---|---|---|---|---|---|---|---|---|
Batch Size | Epoch | Train Accuracy | Test Accuracy | AUC | Precision | Recall | F1 Score | Specificity |
4 | 7 | 0.94 | 0.75 | 0.75 | 0.75 | 0.75 | 0.75 | 0.75 |
15 | 0.98 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | |
20 | 0.98 | 0.75 | 0.75 | 0.75 | 0.75 | 0.75 | 0.75 |
Pretrained EfficientNetB0 on Augmented ORIGA Dataset | ||||||||
---|---|---|---|---|---|---|---|---|
Epoch | Batch Size | Train Accuracy | Test Accuracy | AUC | Precision | Recall | F1 Score | Specificity |
15 | 8 | 0.992 | 0.91 | 0.91 | 0.91 | 0.91 | 0.91 | 0.97 |
16 | 0.995 | 0.89 | 0.89 | 0.89 | 0.89 | 0.89 | 0.94 | |
32 | 0.999 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | |
64 | 0.995 | 0.81 | 0.81 | 0.81 | 0.81 | 0.81 | 0.84 |
Pretrained EfficientNetB0 on Augmented RIM-ONE DL Dataset | ||||||||
---|---|---|---|---|---|---|---|---|
Epoch | Batch Size | Train Accuracy | Test Accuracy | AUC | Precision | Recall | F1 Score | Specificity |
15 | 8 | 0.999 | 0.99 | 0.99 | 0.99 | 0.99 | 0.99 | 1.00 |
16 | 0.999 | 0.93 | 0.93 | 0.93 | 0.93 | 0.93 | 0.93 | |
32 | 0.998 | 0.92 | 0.92 | 0.92 | 0.92 | 0.92 | 0.93 | |
64 | 0.998 | 0.73 | 0.73 | 0.73 | 0.73 | 0.73 | 0.75 |
Pretrained EfficientNetB0 on Augmented ORIGA Dataset | ||||||||
---|---|---|---|---|---|---|---|---|
Batch Size | Epoch | Train Accuracy | Test Accuracy | AUC | Precision | Recall | F1 Score | Specificity |
32 | 10 | 0.995 | 0.95 | 0.95 | 0.95 | 0.95 | 0.95 | 0.97 |
15 | 0.999 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | |
20 | 0.998 | 0.89 | 0.89 | 0.89 | 0.89 | 0.89 | 0.88 |
Pretrained EfficientNetB0 on Augmented RIM-ONE DL Dataset | ||||||||
---|---|---|---|---|---|---|---|---|
Batch Size | Epoch | Train Accuracy | Test Accuracy | AUC | Precision | Recall | F1 Score | Specificity |
8 | 10 | 0.999 | 0.98 | 0.98 | 0.98 | 0.98 | 0.98 | 0.98 |
15 | 0.999 | 0.99 | 0.99 | 0.99 | 0.99 | 0.99 | 1.00 | |
17 | 0.991 | 0.98 | 0.98 | 0.98 | 0.98 | 0.98 | 0.98 |
ORIGA | RIM-ONE DL | HRF | |
---|---|---|---|
Epoch | 15 | 15 | 15 |
Batch size | 32 | 8 | 4 |
Train accuracy | 0.999 | 0.999 | 0.98 |
Test Accuracy | 1.00 | 0.99 | 1.00 |
AUC | 1.00 | 0.99 | 1.00 |
Precision | 1.00 | 0.99 | 1.00 |
Recall | 1.00 | 0.99 | 1.00 |
F1 score | 1.00 | 0.99 | 1.00 |
Specificity | 1.00 | 1.00 | 1.00 |
Performance Criteria | ||||||||
---|---|---|---|---|---|---|---|---|
Study | Dataset | Accuracy | Sensitivity (Recall) | Specificity | AUC | F-Measure | IOU | Computational Efficiency |
[1] | ORIGA | Train: 98.82% Test: 96.90% | Train: 98.95% Test: 97.03% | Train: 98.15% Test: 96.33% | NA | NA | NA | High processing time and memory usage. |
[2] | ORIGA HRF | NA | Avg. 96.3% | NA | NA | 0.97% | 0.97% | Execution time of 1067 s; 6.2 million parameters, making it more memory efficient. |
[3] | ORIGA | 95.75% | 94.75% | 94.9% | 97.9% | NA | NA | EfficientDet-D0: Designed to minimize memory usage while maintaining high accuracy. |
[9] | ORIGA HRF RIM-ONE DL | 97.2% 98.21% 97.96% | 97% | NA | 98% | NA | NA | EfficientDet-D0: Designed to minimize memory usage while maintaining high accuracy. |
Our Proposed Model | ORIGA | Train: 99.9% Test: 100% | 100% | 100% | 100% | 100% | 98% | The training times were as follows: approximately 240 s for ORIGA, 150 s for RIM ONE-DL, and 270 s for HRF. Our model is optimized for high efficiency on Google Colab Pro, utilizing GPU and high RAM, with 4,008,829 trainable parameters. |
RIM-ONE DL | Train: 99.9% Test: 99% | 99% | 100% | 99% | 99% | 98% | ||
HRF | Train: 98% Test: 100% | 100% | 100% | 100% | 100% | 98% |
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Share and Cite
Alkhaldi, N.A.; Alabdulathim, R.E. Optimizing Glaucoma Diagnosis with Deep Learning-Based Segmentation and Classification of Retinal Images. Appl. Sci. 2024, 14, 7795. https://doi.org/10.3390/app14177795
Alkhaldi NA, Alabdulathim RE. Optimizing Glaucoma Diagnosis with Deep Learning-Based Segmentation and Classification of Retinal Images. Applied Sciences. 2024; 14(17):7795. https://doi.org/10.3390/app14177795
Chicago/Turabian StyleAlkhaldi, Nora A., and Ruqayyah E. Alabdulathim. 2024. "Optimizing Glaucoma Diagnosis with Deep Learning-Based Segmentation and Classification of Retinal Images" Applied Sciences 14, no. 17: 7795. https://doi.org/10.3390/app14177795