Deep learning is the most technology in
21century, it gives more information about how
computers can understand data and learning from. In
deep learning, networks of artificial neurons analyse
large dataset to automatically discover patterns. In this
paper, we will introduce the part of these techniques to
know how we can use deep learning to create our own
model to diagnosis eye diseases. The most idea will be
addressed is the evaluation performance model using
confusion matrix. In this study, we will compare three
models of neural network, CNN, Vgg16 and Inceptionv3
in order to evaluate performance of the models.
In 0ur work, a deep learning convolutional
network based on keras and tensorflow is deployed
using python for image classification. a number of
different images, which contains four types of eye
diseases, namely Diabetic retinopathy, Glaucoma,
Myopia and Normal are used for image classification.
Three different structures of neural network, CNN,
VGG16 and Inception V3 are compared on GPU system
in Google Colab, with three different combinations of
classifiers. It is shown that, the results for each
combination and observed that for multi-image
classification, Inception V3 combination gives better
classification accuracy (81.00 %) than any other models.
Using of confusion matrix showing us where our
classifier is confused when it makes prediction.
Keywords : Inception V3, CNN, Vgg16, Eye Diseases, Confusing Matrix, Deep Learning, Diabetic Retinopathy, Glaucoma, Myopia.