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Retinal Disease Diagnosis Using Deep Learning on Ultra-Wide-Field Fundus Images.

Toan Duc Nguyen1, Duc-Tai Le2, Junghyun Bum3

  • 1Department of AI Systems Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea.

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|January 11, 2024
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Summary

This study shows ResNet152 deep learning model effectively detects eye diseases using ultra-wide-field fundus imaging (UFI). The automated system achieved 96.47% AUC, aiding ophthalmologists in diagnosis.

Keywords:
convolutional neural networkdeep learningfundus imagemedical image processingvision transformer

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Area of Science:

  • Ophthalmology and Medical Imaging
  • Artificial Intelligence in Healthcare
  • Computer Vision

Background:

  • Ultra-wide-field fundus imaging (UFI) offers comprehensive visualization of ocular structures.
  • Accurate diagnosis of eye diseases relies on detailed fundus examination.
  • Deep learning presents opportunities for automated analysis of medical images.

Purpose of the Study:

  • To investigate the efficacy of deep learning models for eye disease detection using UFI.
  • To develop an automated system for processing and analyzing UFI data.
  • To compare the performance of various deep learning architectures for this task.

Main Methods:

  • An automated system was developed using a dataset of 4697 UFI images.
  • Image enhancement techniques (brightness, contrast) were applied.
  • Convolutional neural networks (CNNs) with feature extraction, data augmentation, and transfer learning were employed.
  • Five models (ResNet152, Vision Transformer, InceptionResNetV2, RegNet, ConVNext) were evaluated.

Main Results:

  • ResNet152 demonstrated the highest performance, achieving an Area Under the Curve (AUC) of 96.47% (95% CI: 0.931-0.974).
  • Visualizations including confidence scores and heatmaps were generated to interpret model predictions.
  • The system successfully identified focal points of lesions in the medical images.

Conclusions:

  • ResNet152 is highly effective for automated eye disease detection from UFI.
  • The developed system streamlines diagnosis and provides detailed prediction insights.
  • UFI combined with deep learning shows significant potential for ophthalmological applications.