phat 18: e9

Research Article

Deep Learning Based Analysis of Ophthalmology: A Systematic Review

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  • @ARTICLE{10.4108/eai.10-9-2021.170950,
        author={Puneet Kumar and R. Kumar and M. Gupta},
        title={Deep Learning Based Analysis of Ophthalmology: A Systematic Review},
        journal={EAI Endorsed Transactions on Pervasive Health and Technology: Online First},
        volume={},
        number={},
        publisher={EAI},
        journal_a={PHAT},
        year={2021},
        month={9},
        keywords={Deep Learning, Machine Learning, Diabetic Retinopathy, Age-related Macular Degeneration, Glaucoma},
        doi={10.4108/eai.10-9-2021.170950}
    }
    
  • Puneet Kumar
    R. Kumar
    M. Gupta
    Year: 2021
    Deep Learning Based Analysis of Ophthalmology: A Systematic Review
    PHAT
    EAI
    DOI: 10.4108/eai.10-9-2021.170950
Puneet Kumar1, R. Kumar2, M. Gupta3,*
  • 1: Research Scholar, Computer Science Department, Chandigarh University, Punjab, India
  • 2: Professor, Computer Science Department, Chandigarh University, Punjab, India
  • 3: Associate Professor, Computer Science Department, Chandigarh University, Punjab, India
*Contact email: meenu.e9406@cumail.in

Abstract

INTRODUCTION: Diagnosis of medical-related problems is the biggest issue in every era. In past decades, due to less number of technologies and equipment’s it was challenging to diagnose it. As time passes, technologies i.e. Artificial Intelligence (AI) grow and became popular in the medical field especially in ophthalmology at proliferating rate. But, still, many of the diseases are diagnose manually. (i.e., eye disease disorder) which is time consuming, expensive, and tedious task. Existing prediction systems can resolve medical issues such as ocular disorder but the accuracy of prediction is very less.

OBJECTIVES: This study gives a brief overview of the analysis of traditional systems with modern approaches. Further, this study highlights the different allied techniques and impact of transfer learning on ophthalmology for the detection of various eye diseases i.e., Diabetic Retinopathy (DR), Age-related Macular Degeneration (AMD), Glaucoma, etc.

METHODS: AI and Machine Learning Technique are used to conduct this research for analysis.

RESULTS: The result of this paper concludes AI with allied techniques may reshape and revolutionize the medical community especially in the area of ophthalmology.

CONCLUSION: This paper presented a comprehensive review of AI with allied techniques in ophthalmology. In ML and DL-based approaches, CNN provides the most promising results.