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Machine Learning Methods for Diagnosis of Eye-Related Diseases: A Systematic Review Study Based on Ophthalmic Imaging Modalities

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Abstract

Glaucoma, diabetic retinopathy, diabetic hypertension (DHR), Cataract, and age-related macular degeneration are some of the most common and important retinal diseases. A permanent vision loss occurs if these diseases are not discovered at an early stage. It is illustrated by numerous abnormalities in the retina such as microaneurysms (MA), hard exudates, soft exudates, or cotton wool spots, hemorrhages (HEM), neovascularization (NV), and macular edema (DME). An analysis of Ophthalmic imaging modalities is used as computerized tools by ophthalmologists for faster screening and diagnosis of these diseases. Compared to traditional-machine learning, multilayer deep learning (MDL), speeds up diagnosis and improves precision and accuracy. As a result, this survey offers a thorough overview of the new technologies used in each phase of the retinal diagnosis process concerning various image modalities. Among different imaging modalities, this paper is primarily focused on the retinal fundus photograph and optical coherence tomography images. This review aims to provide a more in-depth survey of the most significant components, types, and network architectures of MDL algorithms. This paper also describes the advantage of using other different MDL algorithms that are not being used in the past. To assist the researcher, the challenges and potential developments of current traditional and deep learning methods are described in depth. At last, the challenges and recommended solutions are also presented to support scientists in terms of research gaps. We have measured the influence of benchmarks (GPU, and CPU) on MDL algorithms in this paper. Moreover, the limitations and future direction are also mentioned to assist other experts. The comparative results and discussion section of this article suggest that a rigorous MDL model is still required compared to traditional machine-learning to effectively diagnose eye-related diseases in several modalities.

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Acknowledgements

This research was supported by the Deanship of Scientific Research, Imam Mohammad Ibn Saud Islamic University (IMSIU), Saudi Arabia, Grant No. (20-13-09-002).

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  1. College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), P.O. Box 5701, Riyadh, 11432, Saudi Arabia

    Qaisar Abbas

  2. Key Laboratory of Space Photoelectric Detection and Perception, Ministry of Industry and Information Technology and College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, Jiangsu, China

    Imran Qureshi & Junhua Yan

  3. School of Computer Science and Engineering, South China University of Technology, Guangzhou, 510006, China

    Kashif Shaheed

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Abbas, Q., Qureshi, I., Yan, J. et al. Machine Learning Methods for Diagnosis of Eye-Related Diseases: A Systematic Review Study Based on Ophthalmic Imaging Modalities. Arch Computat Methods Eng 29, 3861–3918 (2022). https://doi.org/10.1007/s11831-022-09720-z

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