International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

Deep Learning Technique for Detection of Myopic Disorders

Abstract

The prevalence of myopia, a common refractive error causing blurred distant vision, has been steadily increasing. In more severe cases, myopia manifests as high myopia and pathological myopia, which can lead to irreversible vision impairment due to associated complications like retinal detachment and macular degeneration. High myopia and pathological myopia pose serious threats to visual health, necessitating accurate and early detection for effective intervention. This research focuses on leveraging Convolutional Neural Networks (CNNs) for the automated detection and classification of high myopia and pathological myopia from fundus images. CNNs have proven to be powerful tools in image analysis tasks, particularly in discerning intricate patterns and features. Fundus photographs and optical coherence tomography scans are employed to capture detailed anatomical structures associated with high myopia and pathological myopia.

Country : India

1 Prof. K. D. Yesugade2 Mayuri Mane3 Sanika Sasane4 Neha Wadkar5 Shreya Yadav

  1. Professor, Department of Computer Engineering, Bharati Vidyapeeth’s College of Engineering for Women, Pune, India
  2. Student, Department of Computer Engineering, Bharati Vidyapeeth’s College of Engineering for Women, Pune, India
  3. Student, Department of Computer Engineering, Bharati Vidyapeeth’s College of Engineering for Women, Pune, India
  4. Student, Department of Computer Engineering, Bharati Vidyapeeth’s College of Engineering for Women, Pune, India
  5. Student, Department of Computer Engineering, Bharati Vidyapeeth’s College of Engineering for Women, Pune, India

IRJIET, Volume 8, Issue 5, May 2024 pp. 63-70

doi.org/10.47001/IRJIET/2024.805009

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