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Deep Learning for Ocular Disease Recognition: An Inner-Class Balance.

Md Shakib Khan1, Nafisa Tafshir1, Kazi Nabiul Alam1

  • 1Department of Electrical and Computer Engineering, North South University, Bashundhara, Dhaka 1229, Bangladesh.

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|May 9, 2022
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Summary
This summary is machine-generated.

This study introduces a deep learning approach using VGG-19 for automated ocular disease detection from fundus images. Balancing the dataset significantly improved the accuracy of identifying conditions like pathological myopia, cataract, and glaucoma.

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Area of Science:

  • Ophthalmology
  • Computer Science
  • Artificial Intelligence

Background:

  • Manual diagnosis of ocular diseases from fundus images is challenging, time-consuming, and prone to errors.
  • Automated ocular disease detection systems are needed to aid clinicians in early and accurate diagnosis.
  • Deep learning advancements offer powerful tools for image classification in medical diagnostics.

Purpose of the Study:

  • To develop and evaluate a deep-learning-based system for detecting ocular diseases using fundus images.
  • To address the challenge of an unbalanced dataset in ocular disease classification.
  • To improve the accuracy and efficiency of ocular disease identification.

Main Methods:

  • Utilized the VGG-19 deep learning model for image classification.
  • Applied a strategy to convert the multiclass ocular disease classification problem into a binary classification problem.
  • Balanced the dataset by ensuring an equal number of images for binary classifications.

Main Results:

  • The VGG-19 model achieved high accuracy in binary classifications: 98.13% for normal vs. pathological myopia, 94.03% for normal vs. cataract, and 90.94% for normal vs. glaucoma.
  • Data balancing significantly improved the accuracy of various classification models.
  • The proposed approach demonstrated the effectiveness of deep learning in ocular disease detection.

Conclusions:

  • A deep learning approach, specifically VGG-19, can effectively detect ocular diseases from fundus images.
  • Dataset balancing is crucial for improving the performance of deep learning models in medical image analysis.
  • The developed system shows promise for aiding in the early and accurate diagnosis of eye disorders.