AI Medical Imaging with ResNet50 for Eye Disease Classification
Eye diseases such as cataracts, diabetic retinopathy, and glaucoma are significant contributors to vision impairment worldwide. Early detection of these conditions is crucial for effective management and treatment. A deep learning-based approach, using models like ResNet50, can significantly aid in the diagnosis of these eye diseases from retinal images, improving both accuracy and efficiency.
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Implementation Highlights
- Utilizes ResNet50, a deep convolutional neural network, for feature extraction and classification of eye conditions.
- Input retinal images are resized to 200×200 pixels to maintain computational efficiency while retaining essential details for classification.
- The model uses pre-trained weights, leveraging knowledge learned from large datasets to improve performance on medical imaging tasks.
- Techniques like rotation, flipping, and scaling are used to expand the dataset and improve model robustness.
Outcome
The model successfully classifies retinal images into categories such as cataract, diabetic retinopathy, glaucoma, and normal. It provides a high degree of accuracy and can assist ophthalmologists in early diagnosis and intervention.