Computer-aided diagnosis of keratoconus through VAE-augmented images using deep learning
Zhila Agharezaei1,2,3, Reza Firouzi4, Samira Hassanzadeh5
1Pharmaceutical Research Center, Pharmaceutical Technology Institute, Mashhad University of Medical Sciences, Mashhad, Iran.
Scientific Reports
|November 23, 2023
Summary
Deep learning models accurately detect keratoconus (KCN) using corneal topography. Synthesized images improved performance, paving the way for advanced computer-aided diagnosis systems in ophthalmology.
Area of Science:
- Ophthalmology
- Artificial Intelligence
- Medical Imaging
Background:
- Keratoconus (KCN) detection is challenging and time-consuming for ophthalmologists.
- Accurate diagnosis relies on reviewing demographic and clinical ophthalmic examinations.
- Corneal topography maps are crucial for KCN assessment.
Purpose of the Study:
- To develop and evaluate deep convolutional neural network (CNN) models for KCN detection.
- To assess the accuracy of AI models using corneal topographic maps.
- To explore the impact of data augmentation on KCN detection performance.
Main Methods:
- Retrospective collection of 1758 corneal images from 1010 subjects.
- Dataset augmentation using Variational Auto Encoder (VAE) to create 4000 samples.
- Training and evaluation of four deep learning models (VGG16, EfficientNet-B0, etc.) on original and synthesized images.
Main Results:
- Deep learning models achieved high accuracy, ranging from 95% (EfficientNet-B0) to 99% (VGG16).
- All models demonstrated sensitivity and specificity above 0.94; VGG16 reached an AUC of 0.99.
- Synthesized images enhanced classification performance during the training process.
- A customized CNN model offered a balance of accuracy (0.97) and speed.
Conclusions:
- Deep learning models show high accuracy for KCN screening using corneal topography.
- AI-driven systems can assist ophthalmologists in clinical decision-making for KCN.
- This technology supports prompt and precise KCN treatment through enhanced computer-aided diagnosis (CAD).
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