Abstract: Eye Disease is a frequent health issue that can cause either partial or total loss of vision. Early diagnosis is essential for eye disease to be effectively treated and managed Deep learning has become a potent tool for the detection and diagnosis of numerous medical disorders, including eye diseases, in recent years. Convolutional Neural Network (CNN) is a deep learning technique that is often used for image analysis and pattern recognition. In this approach, we use a CNN based method for classifying various eye diseases. The suggested method extracts feature from eye images and categorizes them using CNN.A dataset of eye images. collected, including images of healthy eyes. well as diseased eyes which are Cataracts, Glaucoma, Bulging eyes, Uveitis, and Crossed Eyes. The dataset underwent preprocessing to normalize the images and get rid of any noise. The training and testing sets were then created from the pre-processed dataset. A CNN model is trained using the pre-processed images, and it is then tuned using one among the many fine-tuning methods. The proposed approach will achieve high accuracy in detecting eye conditions. The results indicate that this approach can be a valuable tool in the early detection and diagnosis of eye conditions, which can improve the outcomes of treatment and prevent vision loss.
Keywords: Convolutional neural networks, Cateract, Dataset, preprocessing.
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DOI:
10.17148/IJARCCE.2024.134129
