Glaucoma Detection Using Deep Learning
Glaucoma Detection Using Deep Learning uses AI to find early signs of glaucoma in eye images. This helps doctors diagnose the disease quickly and prevent vision loss.
$15 USD
$5.00 USD
Project Outcomes
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This project achieves 89% accuracy using a custom CNN model
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Constructed a Vision Transformer model to exhibit its application in the field of medical image processing.
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Three different architectures (Vision Transformer, Custom CNN, and VGG16) have been compared in terms of their advantages and disadvantages.
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Enhanced model generalization by employing more image preprocessing techniques such as image resizing, normalization, and augmentation.
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Explored deep learning in health care to help ophthalmologists in the treatment of glaucoma.
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Addressed image imbalance and enhanced the diversity of the dataset by implementing data augmentation methods.
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Presented confusion matrices and generated accuracy/loss graphs to visualize how well the model has been trained.
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Incorporated steps in the process of glaucoma diagnosis to lessen the burden on medical specialists and improve early disease detection.
- The project contributes to the growth of AI in medical imaging.
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