Machine Learning-Based Detection and Classification of Eye Diseases: A Comprehensive Review and Novel Algorithm
Keywords:
Machine Learning, Eye Diseases, Disease Detection, Disease Classification, Ophthalmology, Retinal Imaging, Vision Transfer Method, Diagnostic AlgorithmsAbstract
Machine learning has transformed the landscape of ophthalmology, offering a powerful approach for automating and improving the detection and classification of eye diseases. This comprehensive review delves into the current state of the field, emphasizing the potential and challenges. Central to this review is the concept of the "Vision Transfer Method," a novel approach that leverages the transfer of learned visual knowledge to enhance disease detection and classification. We explore the utilization of the Vision Transfer Method in the analysis of diverse ophthalmic imaging data, encompassing retinal images, optical coherence tomography (OCT) scans, and fundus photographs. Our analysis underscores the critical need for extensive and diverse datasets and the interpretability of machine learning models in clinical practice. Ethical considerations and regulatory compliance are discussed, ensuring responsible implementation of this transformative technology. Additionally, this paper introduces a novel diagnostic algorithm based on the Vision Transfer Method, poised to significantly enhance diagnostic accuracy and early disease identification, ultimately contributing to improved patient outcomes in the domain of ophthalmology.
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M. Kamal, H. I. Shanto, M. M. Hossan, and A. Hasnat, “A Comprehensive Review on the Diabetic Retinopathy, Glaucoma and Strabismus Detection Techniques Based on Machine Learning and Deep Learning,” Eur. J. Med. Heal. Sci., vol. 4, no. 2, pp. 24–40, 2022, doi: 10.34104/ejmhs.022.024040.
S. M. Sarsam and H. Al-Samarraie, “A lexicon-based method for detecting eye diseases on microblogs,” Appl. Artif. Intell., vol. 36, no. 1, 2022, doi: 10.1080/08839514.2021.1993003.
F. Abdullah et al., “A Review on Glaucoma Disease Detection Using Computerized Techniques,” IEEE Access, vol. 9, pp. 37311–37333, 2021, doi: 10.1109/ACCESS.2021.3061451.
Y. Roy, H. Banville, I. Albuquerque, A. Gramfort, T. H. Falk, and J. Faubert, “Deep learning-based electroencephalography analysis: A systematic review,” J. Neural Eng., vol. 16, no. 5, 2019, doi: 10.1088/1741-2552/ab260c.
M. Reichstein et al., “Deep learning and process understanding for data-driven Earth system science,” Nature, vol. 566, no. 7743, pp. 195–204, 2019, doi: 10.1038/s41586-019-0912-1.
L. Alzubaidi et al., Review of deep learning: concepts, CNN architectures, challenges, applications, future directions, vol. 8, no. 1. Springer International Publishing, 2021.
Y. Tong, W. Lu, Y. Yu, and Y. Shen, “Application of machine learning in ophthalmic imaging modalities,” Eye Vis. 2020 71, vol. 7, no. 1, pp. 1–15, Apr. 2020, doi: 10.1186/S40662-020-00183-6.
R. Vij and S. Arora, “A Systematic Review on Diabetic Retinopathy Detection Using Deep Learning Techniques,” Arch. Comput. Methods Eng., vol. 30, no. 3, pp. 2211–2256, 2023, doi: 10.1007/s11831-022-09862-0.
C. Y. Cheung et al., “A deep learning model for detection of Alzheimer’s disease based on retinal photographs: a retrospective, multicentre case-control study,” Lancet Digit. Heal., vol. 4, no. 11, pp. e806–e815, 2022, doi: 10.1016/S2589-7500(22)00169-8.
S. Malik, N. Kanwal, M. N. Asghar, M. A. A. Sadiq, I. Karamat, and M. Fleury, “Data driven approach for eye disease classification with machine learning,” Appl. Sci., vol. 9, no. 14, 2019, doi: 10.3390/app9142789.
N. M. Dipu, “Network Based Classification Algorithms,” vol. VII, no. Ii, pp. 91–99.
P. Kumar, R. Kumar, and M. Gupta, “Deep Learning Based Analysis of Ophthalmology: A Systematic Review,” EAI Endorsed Trans. Pervasive Heal. Technol., vol. 7, no. 29, 2021, doi: 10.4108/eai.10-9-2021.170950.
S. O. Fageeri, S. M. M. Ahmed, S. A. Almubarak, and A. A. Mu’Azu, “Eye refractive error classification using machine learning techniques,” Proc. - 2017 Int. Conf. Commun. Control. Comput. Electron. Eng. ICCCCEE 2017, no. February, 2017, doi: 10.1109/ICCCCEE.2017.7867660.
R. Nuzzi, G. Boscia, P. Marolo, and F. Ricardi, “The Impact of Artificial Intelligence and Deep Learning in Eye Diseases: A Review,” Front. Med., vol. 8, no. August, pp. 1–11, 2021, doi: 10.3389/fmed.2021.710329.
[15] Y. Kumar, A. Koul, R. Singla, and M. F. Ijaz, “Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda,” J. Ambient Intell. Humaniz. Comput., vol. 14, no. 7, pp. 8459–8486, 2023, doi: 10.1007/s12652-021-03612-z.
N. Tsiknakis et al., “Deep learning for diabetic retinopathy detection and classification based on fundus images: A review,” Comput. Biol. Med., vol. 135, p. 104599, 2021, doi: 10.1016/j.compbiomed.2021.104599.
P. Glaret subin and P. Muthukannan, “Optimized convolution neural network based multiple eye disease detection,” Comput. Biol. Med., vol. 146, no. January, p. 105648, 2022, doi: 10.1016/j.compbiomed.2022.105648.
M. S. Khan et al., “Deep Learning for Ocular Disease Recognition: An Inner-Class Balance,” Comput. Intell. Neurosci., vol. 2022, 2022, doi: 10.1155/2022/5007111.
L. A. Passos, D. Jodas, K. A. P. da Costa, L. A. S. Júnior, D. Colombo, and J. P. Papa, “A Review of Deep Learning-based Approaches for Deepfake Content Detection,” 2022, [Online]. Available: http://arxiv.org/abs/2202.06095.
L. Dai et al., “A deep learning system for detecting diabetic retinopathy across the disease spectrum,” Nat. Commun., vol. 12, no. 1, 2021, doi: 10.1038/s41467-021-23458-5.
M. M. Ahsan, S. A. Luna, and Z. Siddique, “Machine-Learning-Based Disease Diagnosis: A Comprehensive Review,” Healthc., vol. 10, no. 3, pp. 1–30, 2022, doi: 10.3390/healthcare10030541.
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