Worldwide there are about 1 billion people affected by some kind of vision impairment. Many suffer from eye diseases like
cataract, glaucoma or retina diseases.
We have trained a deep learning model to detect these three different eye diseases with a very small dataset of about 300 image files. The data comes from a kaggle competition and can be downloaded here. This is a very small dataset for training a deep neural net. Therefore we have used a technology called transfer learning to improve training time and accuracy for small training datasets.
Though we have very less training examples to train a deep neurol net, we have achieved good results by use of a technique called
Transfer Learning. This means, we use a pretrained model like ResNet34, cut the last layer, add a new dense layer
with 4 output nodes and only train the last layer.
With this process our model can profit from the pretrained basic layers and we can achieve better results in a shorter periode of time.
Please be aware of the fact, that this model is used for demonstrations only. It's not meant for real diagnosis of eye diseases. If you think you suffer from any vision impairment, consult your ophthalmologist!
If you are more interested in how we implemented the deep learning model have a look at the
jupyter notebook in the repository.
If you are curious on how to host a deep learning model see our blog post on medium: put-deep-learning-image-classifier-in-action
If you need some example images of eye diseases to test our mordel, search for terms like "image retina disease": duckduckgo.com
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