Survey on Diagnosing Retina Diseases in Optical Coherence Tomography Images Based on Deep Learning
DOI:
https://doi.org/10.29304/jqcsm.2024.16.21568Keywords:
Retina Diseases, OCT Images, Diabetic Macular Edema, Choroidal Neovascularization, Drusen, Deep LearningAbstract
This study provides a comprehensive and extensive review of the use of deep learning techniques in diagnosing a variety of retinal diseases, such as Drusen, choroidal neovascularization (CNV), and diabetic macular edema (DME) using optical coherence tomography images (OCT). The research reviews the different models used in this field and evaluates their effectiveness, robustness, and reliability in distinguishing and diagnosing retinal diseases with high accuracy. The research analyzes the most effective and most widely used models in this field, focusing on the results achieved in terms of accuracy and reliability, highlighting the methods that showed the best performance in diagnosis, and improving and developing them in the future to achieve better results. The specialized data sets used to train and test these models are also reviewed, with an assessment of their role and importance in promoting and supporting scientific research in this field. Furthermore, the paper discusses recent advances in research that have been achieved in recent years. In addition, the research addresses current research gaps and challenges facing researchers and provides a comprehensive vision of future work that can contribute to the further development of this field. One of the topics that the research aims for is the importance of using deep learning techniques in the medical field to enhance the accuracy and speed of diagnosis. The research concludes by providing recommendations on future directions that research in this field can take, with the aim of achieving sustainable progress and improving the quality of healthcare for patients with retinal diseases using deep learning techniques
Downloads
References
Di. Rastogi, R. P. Padhy, and P. K. Sa, “Detection of Retinal Disorders in Optical Coherence Tomography using Deep Learning,” 2019 10th International Conference on Computing, Communication and Networking Technologies, ICCCNT 2019, Jul. 2019, doi: 10.1109/ICCCNT45670.2019.8944406.
M. R. Ibrahim, K. M. Fathalla, and S. M. Youssef, “HyCAD-OCT: A hybrid computer-aided diagnosis of retinopathy by optical coherence tomography integrating machine learning and feature maps localization,” Applied Sciences (Switzerland), vol. 10, no. 14, Jul. 2020, doi: 10.3390/app10144716.
L. Fang, C. Wang, S. Li, H. Rabbani, X. Chen, and Z. Liu, “Attention to lesion: Lesion-Aware convolutional neural network for retinal optical coherence tomography image classification,” IEEE Trans Med Imaging, vol. 38, no. 8, pp. 1959–1970, Aug. 2019, doi: 10.1109/TMI.2019.2898414.
J. Ong, A. Zarnegar, G. Corradetti, S. R. Singh, and J. Chhablani, “Advances in Optical Coherence Tomography Imaging Technology and Techniques for Choroidal and Retinal Disorders,” Journal of Clinical Medicine, vol. 11, no. 17. MDPI, Sep. 01, 2022. doi: 10.3390/jcm11175139.
R. K. Ara, A. Matiolański, A. Dziech, R. Baran, P. Domin, and A. Wieczorkiewicz, “Fast and Efficient Method for Optical Coherence Tomography Images Classification Using Deep Learning Approach,” Sensors, vol. 22, no. 13, Jul. 2022, doi: 10.3390/s22134675.
D. S. Kermany et al., “Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning,” Cell, vol. 172, no. 5, pp. 1122-1131.e9, Feb. 2018, doi: 10.1016/j.cell.2018.02.010.
M. Subramanian et al., “Diagnosis of Retinal Diseases Based on Bayesian Optimization Deep Learning Network Using Optical Coherence Tomography Images,” Comput Intell Neurosci, vol. 2022, 2022, doi: 10.1155/2022/8014979.
Y. Rong et al., “Surrogate-Assisted Retinal OCT Image Classification Based on Convolutional Neural Networks,” IEEE J Biomed Health Inform, vol. 23, no. 1, pp. 253–263, Jan. 2019, doi: 10.1109/JBHI.2018.2795545.
A. Adel, M. M. Soliman, N. E. M. Khalifa, and K. Mostafa, “Automatic Classification of Retinal Eye Diseases from Optical Coherence Tomography using Transfer Learning,” in 16th International Computer Engineering Conference, ICENCO 2020, Institute of Electrical and Electronics Engineers Inc., Dec. 2020, pp. 37–42. doi: 10.1109/ICENCO49778.2020.9357324.
A. Choudhary, S. Ahlawat, S. Urooj, N. Pathak, A. Lay-Ekuakille, and N. Sharma, “A Deep Learning-Based Framework for Retinal Disease Classification,” Healthcare (Switzerland), vol. 11, no. 2, Jan. 2023, doi: 10.3390/healthcare11020212.
A. Ram and C. C. Reyes-Aldasoro, “The relationship between Fully Connected Layers and number of classes for the analysis of retinal images,” Apr. 2020, Accessed: Sep. 28, 2023. [Online]. Available: https://arxiv.org/abs/2004.03624v2
P. Dutta, K. A. Sathi, M. A. Hossain, and M. A. A. Dewan, “Conv-ViT: A Convolution and Vision Transformer-Based Hybrid Feature Extraction Method for Retinal Disease Detection,” J Imaging, vol. 9, no. 7, Jul. 2023, doi: 10.3390/jimaging9070140.
N. Ferrara, “Vascular endothelial growth factor and age-related macular degeneration: from basic science to therapy,” Nature Medicine 2010 16:10, vol. 16, no. 10, pp. 1107–1111, 2010, doi: 10.1038/nm1010-1107.
R. Varma et al., “Prevalence of and risk factors for diabetic macular edema in the United States,” JAMA Ophthalmol, vol. 132, no. 11, pp. 1334–1340, Nov. 2014, doi: 10.1001/JAMAOPHTHALMOL.2014.2854.
D. S. Friedman et al., “Prevalence of age-related macular degeneration in the United States,” Arch Ophthalmol, vol. 122, no. 4, pp. 564–572, Apr. 2004, doi: 10.1001/ARCHOPHT.122.4.564.
M. Toğaçar, B. Ergen, and V. Tümen, “Use of dominant activations obtained by processing OCT images with the CNNs and slime mold method in retinal disease detection,” Biocybern Biomed Eng, vol. 42, no. 2, pp. 646–666, Apr. 2022, doi: 10.1016/j.bbe.2022.05.005.
J. Kim and L. Tran, “Retinal disease classification from OCT images using deep learning algorithms,” 2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2021, 2021, doi: 10.1109/CIBCB49929.2021.9562919.
M. T. Reza, F. Ahmed, S. Sharar, and A. A. Rasel, “Interpretable Retinal Disease Classification from OCT Images Using Deep Neural Network and Explainable AI,” in Proceedings of International Conference on Electronics, Communications and Information Technology, ICECIT 2021, Institute of Electrical and Electronics Engineers Inc., 2021. doi: 10.1109/ICECIT54077.2021.9641066.
K. T. Islam, S. Wijewickrema, and S. O’Leary, “Identifying diabetic retinopathy from OCT images using deep transfer learning with artificial neural networks,” in Proceedings - IEEE Symposium on Computer-Based Medical Systems, Institute of Electrical and Electronics Engineers Inc., Jun. 2019, pp. 281–286. doi: 10.1109/CBMS.2019.00066.
V. Latha and K. G. Sreeni, “OCT Image-Based Macular Disease Classification Using Multilayer Deep Feature Fusion,” in 2023 International Conference on Control, Communication and Computing, ICCC 2023, Institute of Electrical and Electronics Engineers Inc., 2023. doi: 10.1109/ICCC57789.2023.10165627.
A. Roy, R. Abdullah, F. Ahmed, S. Mashfi, S. H. Khan, and D. Z. Karim, “RetNet: Retinal Disease Detection using Convolutional Neural Network,” in 3rd International Conference on Electrical, Computer and Communication Engineering, ECCE 2023, Institute of Electrical and Electronics Engineers Inc., 2023. doi: 10.1109/ECCE57851.2023.10101661.
Z. Zhou et al., “Diagnosis of retinal diseases using the vision transformer model based on optical coherence tomography images,” https://doi.org/10.1117/12.2665918, vol. 12601, p. 1260102, Mar. 2023, doi: 10.1117/12.2665918.
L. Mou, L. Liang, Z. Gao, and X. Wang, “A multi-scale anomaly detection framework for retinal OCT images based on the Bayesian neural network,” Biomed Signal Process Control, vol. 75, May 2022, doi: 10.1016/j.bspc.2022.103619.
M. T. Do, H. N. Huynh, T. N. Tran, and T. L. Hoang, “Prediction of Retina Damage in Optical Coherence Tomography Image Using Xception Architecture Model,” in 2023 IEEE 5th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability, ECBIOS 2023, Institute of Electrical and Electronics Engineers Inc., 2023, pp. 58–61. doi: 10.1109/ECBIOS57802.2023.10218586.
P. Elena-Anca, “Applications of Deep Learning algorithms for retinal diseases diagnosis based on Optical Coherence Tomography imaging,” in Proceedings - 2023 24th International Conference on Control Systems and Computer Science, CSCS 2023, Institute of Electrical and Electronics Engineers Inc., 2023, pp. 594–597. doi: 10.1109/CSCS59211.2023.00099.
D. Paul, A. Tewari, S. Ghosh, and K. C. Santosh, “OCTx: Ensembled deep learning model to detect retinal disorders,” in Proceedings - IEEE Symposium on Computer-Based Medical Systems, Institute of Electrical and Electronics Engineers Inc., Jul. 2020, pp. 526–531. doi: 10.1109/CBMS49503.2020.00105.
J. P, K. N, S. S, T. R, and Y. P, “An Enhanced Technique To Classify OCT Images Using Deep Learning,” pp. 1–5, Jun. 2023, doi: 10.1109/ICEEICT56924.2023.10157652.
O. Akinniyi, I. Razzak, M. M. Rahman, H. Sandhu, A. El-Baz, and F. Khalifa, “Multi-Classification of Retinal Diseases Using a Pyramidal Ensemble Deep Framework,” in 2023 IEEE International Conference on Image Processing (ICIP), IEEE, Oct. 2023, pp. 1945–1949. doi: 10.1109/ICIP49359.2023.10222074.
R. S. Khedgaonkar, A. Nagrare, A. Pande, A. Funde, B. Rathi, and A. Bagde, “Retinal Damage Detection Using Conv2D Net,” 2022 International Conference on Emerging Trends in Engineering and Medical Sciences, ICETEMS 2022, pp. 373–377, 2022, doi: 10.1109/ICETEMS56252.2022.10093422.
B. S. Brasil, A. R. De Alexandria, and G. De Freitas Guimaraes, “Artificial Intelligence applied to the classification of retinal diseases in Optical Coherence Tomography images,” in 2022 5th International Conference on Vocational Education and Electrical Engineering: The Future of Electrical Engineering, Informatics, and Educational Technology Through the Freedom of Study in the Post-Pandemic Era, ICVEE 2022 - Proceeding, Institute of Electrical and Electronics Engineers Inc., 2022, pp. 78–83. doi: 10.1109/ICVEE57061.2022.9930121.
M. Berrimi and A. Moussaoui, “Deep learning for identifying and classifying retinal diseases,” in 2020 2nd International Conference on Computer and Information Sciences, ICCIS 2020, Institute of Electrical and Electronics Engineers Inc., Oct. 2020. doi: 10.1109/ICCIS49240.2020.9257674.
N. B. Khalaf, H. K. Aljobouri, and M. S. Najim, “Identification and Classification of Retinal Diseases by Using Deep Learning Models,” in 2023 International Conference on Smart Applications, Communications and Networking, SmartNets 2023, Institute of Electrical and Electronics Engineers Inc., 2023. doi: 10.1109/SmartNets58706.2023.10215740.
O. Akinniyi, M. M. Rahman, H. S. Sandhu, A. El-Baz, and F. Khalifa, “Multi-Stage Classification of Retinal OCT Using Multi-Scale Ensemble Deep Architecture,” Bioengineering, vol. 10, no. 7, Jul. 2023, doi: 10.3390/bioengineering10070823.
R. Mohan, K. Ganapathy, and R. Arunmozhi, “Comparison of the proposed DCNN model with standard CNN architectures for retinal diseases classification,” Journal of Population Therapeutics and Clinical Pharmacology, vol. 29, no. 3, pp. e112–e122, Aug. 2022, doi: 10.47750/jptcp.2022.945.
İ. Kayadibi and G. E. Güraksın, “An Explainable Fully Dense Fusion Neural Network with Deep Support Vector Machine for Retinal Disease Determination,” International Journal of Computational Intelligence Systems, vol. 16, no. 1, Dec. 2023, doi: 10.1007/s44196-023-00210-z.
P. D. Barua et al., “Multilevel deep feature generation framework for automated detection of retinal abnormalities using OCT images,” Entropy, vol. 23, no. 12, Dec. 2021, doi: 10.3390/e23121651.
X. Huang et al., “GABNet: global attention block for retinal OCT disease classification,” Front Neurosci, vol. 17, 2023, doi: 10.3389/fnins.2023.1143422.
F. Li, H. Chen, Z. Liu, X. Zhang, and Z. Wu, “Fully automated detection of retinal disorders by image-based deep learning,” Graefe’s Archive for Clinical and Experimental Ophthalmology, vol. 257, no. 3, pp. 495–505, Mar. 2019, doi: 10.1007/s00417-018-04224-8.
A. Alqudah, A. M. Alqudah, and M. Altantawi, “Artificial Intelligence Hybrid System for Enhancing Retinal Diseases Classification Using Automated Deep Features Extracted from OCT Images,” International Journal of Intelligent Systems and Applications in Engineering, vol. 9, no. 3, pp. 91–100, Sep. 2021, doi: 10.18201/ijisae.2021.236.
S. Asif, K. Amjad, and Qurrat-ul-Ain, “Deep Residual Network for Diagnosis of Retinal Diseases Using Optical Coherence Tomography Images,” Interdiscip Sci, vol. 14, no. 4, pp. 906–916, Dec. 2022, doi: 10.1007/s12539-022-00533-z.
J. Kim and L. Tran, “Ensemble learning based on convolutional neural networks for the classification of retinal diseases from optical coherence tomography images,” in Proceedings - IEEE Symposium on Computer-Based Medical Systems, Institute of Electrical and Electronics Engineers Inc., Jul. 2020, pp. 532–537. doi: 10.1109/CBMS49503.2020.00106.
H. A. Nugroho and R. Nurfauzi, “Convolutional Neural Network for Classifying Retinal Diseases from OCT2017 Dataset,” in ICOIACT 2021 - 4th International Conference on Information and Communications Technology: The Role of AI in Health and Social Revolution in Turbulence Era, Institute of Electrical and Electronics Engineers Inc., 2021, pp. 295–298. doi: 10.1109/ICOIACT53268.2021.9563975.
P. P. Srinivasan et al., “Fully automated detection of diabetic macular edema and dry age-related macular degeneration from optical coherence tomography images,” Biomed Opt Express, vol. 5, no. 10, p. 3568, Oct. 2014, doi: 10.1364/BOE.5.003568.
P. Gholami, P. Roy, M. K. Parthasarathy, and V. Lakshminarayanan, “OCTID: Optical coherence tomography image database,” Computers & Electrical Engineering, vol. 81, p. 106532, Jan. 2020, doi: 10.1016/J.COMPELECENG.2019.106532.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Shibly Hameed Al-Amiry
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
