This project can accurately classify the given input image as the correct class of eye disease. The supplied image can be correctly classified as having one of the eye diseases listed by the ILL EYE IDENTIFIER. This research uses deep learning to classify the images. We have developed many Deep Learning algorithms like CNN, ResNet, MobileNet and YOLOv3.
These are the steps we followed to successfully build the ILL EYE IDENTIFIER.
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Collecting Data: We decided to use Google images to build our dataset. The dataset consists of 1700+ images, including around 200+ images of 10 different types of eye diseases.
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Data cleaning and preparation: We first removed unwanted images and resized them accordingly.
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Choosing and Training the Model: We decided to use Convolutional Neural Network (CNN), ResNet, MobileNet, and Yolov3 to build our model and trained it with some sample test images.
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Evaluating the Model: The metrics used for evaluating our model were accuracy and loss.
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Prediction by a sample of test data was used to test our model and verify its classification.
The model was successfully able to classify most of the diseased eye images correctly.
Around 27.5% of Indians Suffer from vision problems. It's a serious issue and will be more common in upcoming days due to excessive screen time in electronic gadgets. Many people lack access to eye care services, especially those who reside in underdeveloped or distant areas. This may be caused by a shortage of qualified eye care specialists, as well as a lack of infrastructure and other means of transportation. Eye care can be expensive, and many people may not be able to afford the cost of treatment or surgery. Some people may feel anxiety or fear about undergoing surgery, which can prevent them from seeking treatment.