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Pterygium Screening and Lesion Area Segmentation Based on Deep Learning.

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Deep learning models, VGG16 and PSPNet, accurately diagnosed pterygium from eye images. These AI tools can aid self-screening and assist ophthalmologists in diagnosis and surgical planning.

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Area of Science:

  • Ophthalmology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Pterygium is a common ophthalmic condition affecting the anterior segment of the eye.
  • Accurate diagnosis and surgical planning are crucial for effective pterygium management.
  • Current diagnostic methods can be subjective and time-consuming.

Purpose of the Study:

  • To develop and evaluate deep learning models for pterygium diagnosis and segmentation.
  • To assist ophthalmologists in diagnosing pterygium and determining surgical scope.
  • To provide a tool for easy patient self-screening of pterygium.

Main Methods:

  • Collected 367 normal and 367 pterygium anterior segment images.
  • Trained and tested four two-category classification models (AlexNet, VGG16, ResNet18, ResNet50).
  • Developed and evaluated two improved pterygium segmentation models based on PSPNet.

Main Results:

  • The VGG16 model achieved 99% accuracy, 98% kappa value, 98.67% sensitivity, and 99.33% specificity for pterygium diagnosis.
  • The double phase-fusion PSPNet model demonstrated strong segmentation performance with MIOU of 86.57% and MPA of 92.3%.

Conclusions:

  • Deep learning models, particularly VGG16 for classification and PSPNet for segmentation, show high efficacy in pterygium detection.
  • These AI models can significantly aid ophthalmologists in diagnosis and surgical planning.
  • The developed models offer potential for patient self-screening and improved pterygium care.