STM Journals Peer-reviewed research from Consortium e-Learning Network

Research and Reviews : A Journal of Medical Science and Technology Review Article Open Access

Retinal Disease Detection Using Deep CNN

  1. Ajay. V G Department. of CS&E, Sai Vidya Institute of Technology
  2. Varun E Department. of CS&E, Sai Vidya Institute of Technology
  3. Ashith B. P Department. of CS&E, Sai Vidya Institute of Technology
  4. Lakshmikantha K. N Department. of CS&E, Sai Vidya Institute of Technology
  5. R M Mahesh Department. of CS&E, Sai Vidya Institute of Technology

Abstract

Age-related macular degeneration, glaucoma, and diabetic retinopathy are the three main causes of blindness in the globe. To avoid visual loss, early identification and treatment of these disorders are essential. The goal of this research is to create an automated method for detecting retinal diseases by analyzing retinal fundus pictures with machine learning techniques. Python and the Tkinter package for the graphical user interface are used in the construction of the system. High accuracy is attained by the trained ResNet model in differentiating between normal and aberrant retinal pictures. The model provides disease prediction and classification results for a given input fundus image through an intuitive and user-friendly interface. By delivering rapid and automated screening, this system plays a crucial role in the early diagnosis of retinal abnormalities. Early detection is vital for initiating timely treatment, which can significantly reduce the risk of vision impairment and other complications. This advanced technology supports healthcare professionals in making informed decisions and offers a reliable tool for routine screenings, ultimately enhancing patient outcomes and contributing to the prevention of severe vision loss.

Keywords

References (12)

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