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Diagnosis of Retinal Diseases Based on Bayesian Optimization Deep Learning Network Using Optical Coherence Tomography

Malliga Subramanian1, M Sandeep Kumar2, V E Sathishkumar3

  • 1Department of Computer Science Engineering, Kongu Engineering College, Perundurai, Erode 638060, Tamil Nadu, India.

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This study uses deep learning models like DenseNet201 to detect seven retinal diseases from Optical Coherence Tomography (OCT) images with over 99% accuracy, enabling earlier and more precise diagnosis.

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Retinal abnormalities pose a significant public health risk, potentially leading to vision impairment and blindness.
  • Early and accurate detection of retinal diseases is crucial for effective treatment and prevention of vision loss.
  • Current diagnostic methods may not always provide the necessary precision or early detection.

Purpose of the Study:

  • To investigate the efficacy of transfer learning using pretrained Convolutional Neural Network (CNN) models for detecting retinal diseases.
  • To classify seven different retinal diseases using Optical Coherence Tomography (OCT) images.
  • To compare the performance of various CNN models and identify the most accurate approach.

Main Methods:

  • Utilized pretrained CNN models: VGG16, DenseNet201, InceptionV3, and Xception.
  • Applied transfer learning to classify retinal diseases from a dataset of OCT images.
  • Employed Bayesian optimization for hyperparameter tuning and image augmentation to enhance model generalization.

Main Results:

  • DenseNet201 achieved an accuracy exceeding 99% in classifying retinal diseases from OCT images.
  • The developed models demonstrated high accuracy in differentiating between healthy and diseased retinal images.
  • A comparative analysis showed DenseNet201 outperformed other evaluated CNN models.

Conclusions:

  • Transfer learning with CNNs, particularly DenseNet201, offers a highly accurate automated approach for retinal disease detection.
  • This method provides a significant advancement over existing techniques, enabling classification of multiple retinal diseases.
  • The findings support the potential of AI-driven tools for early and precise diagnosis of vision-threatening retinal conditions.