A Framework for Multiclass Classification of Eye disease Using Deep Learning
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Keywords:
Eye Disease, Deep Learning, VGG-16, Medical Images, AMDAbstract
Eye diseases must be found and treated early to prevent problems with vision and ensure the
right treatment is given. A model based on deep learning and the VGG-16 structure is suggested in this
study for the automatic detection of six common ophthalmic conditions: AMD, Cataract, Diabetic
Retinopathy, Glaucoma, Retinal Detachment and Normal. Kaggle datasets that were made freely
available were selected for this study and used in a split of 80% for training and 20% for testing. In order
to match the VGG-16 model and improve image quality, we resized the images to 227×227 pixels, used
ImageNet statistics for normalization and added Gaussian blur to filter out noise. The model achieved a
total accuracy of 95%, macro and weighted average precision, recall and F1-scores were all recorded as
0.96 and 0.95. This confirms that the model can correctly detect a wide array of eye diseases in fundus
images, suggesting it will be useful for both early screening and automatic diagnosis in ophthalmology.
Although the model produced excellent results for all eye disease. This research shows that VGG-16 and
other deep learning techniques can greatly benefit medical image analysis and aid doctors in making
decisions about patients in ophthalmology.
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