Dianet: a deep learning based architecture to diagnose diabetes using retinal images only
A method of training a convolutional neural network model to predict diabetes from an image of a retina is provided. The method of training a convolutional neural network includes processing a first dataset, wherein processing the first dataset comprises: extracting a circular region from a retinal image, resizing the circular region, cropping the circular region, and placing the circular region onto a black background; training an initial model using a second dataset to yield a first model; training the first model using a third dataset to yield a second model; and training the second model using the first dataset to yield a third model.
1 . A method of training a convolutional neural network model to predict diabetes from an image of a retina, comprising:
processing a first dataset, wherein processing the first dataset comprises:
extracting a circular region from a retinal image;
resizing the circular region;
cropping the circular region; and
placing the circular region onto a black background;
training an initial model using a second dataset to yield a first model;
training the first model using a third dataset to yield a second model; and
training the second model using the first dataset to yield a third model-,
wherein the third dataset comprises a plurality of retinal images labeled based on the severity of diabetic retinopathy.
2 . The method of training a convolutional neural network model to predict diabetes from the image of a retina of claim 1 , wherein processing the first dataset outputs an image having a retina with a radius of 300 pixels.
3 . The method of training a convolutional neural network model to predict diabetes from the image of a retina of claim 1 , wherein the initial model is a DenseNet model.
4 . The method of training a convolutional neural network model to predict diabetes from the image of a retina of claim 3 , wherein the DenseNet model is a 121-layer variant DenseNet model.
5 . The method of training a convolutional neural network model to predict diabetes from the image of a retina of claim 1 , wherein the first dataset comprises a plurality of retinal images including a first group of retinal images and a second group of retinal images.
6 . The method of training a convolutional neural network model to predict diabetes from the image of a retina of claim 5 , wherein the first group of retinal images relates to a control group and the second group of retinal images relates to a diabetes group.
7 . A convolutional neural network model architecture to predict diabetes from an image of a retina, comprising:
a DenseNet-121 backbone;
a final layer outputting a predictive label corresponding to a diabetic prediction or a non-diabetic prediction;
a pair of pooling layers; and
a first composite layer, a second composite layer, and a third composite layer,
wherein the pair of pooling layers comprise a global average pooling layer and a global max pooling layer.
8 . The convolutional neural network model architecture of claim 7 , wherein the final layer is a single neuron layer.
9 . The convolutional neural network model architecture of claim 7 , wherein the first composite layer comprises a first sequence of a plurality of layers, the second composite layer comprises a second sequence of a plurality of layers, and the third composite layer comprises a third sequence of a plurality of layers.
10 . The convolutional neural network model architecture of claim 9 , wherein the first sequence of the plurality of layers comprises a batch normalization layer, a dropout layer, a linear layer, and a rectified linear unit layer.
11 . The convolutional neural network model architecture of claim 9 , wherein the second sequence of the plurality of layers comprises a batch normalization layer, a dropout layer, a linear layer, and a rectified linear unit layer.
12 . The convolutional neural network model architecture of claim 9 , wherein the third sequence of the plurality of layers comprises a batch normalization layer, a dropout layer, and the final layer for outputting the predictive label.
13 . A method of training a convolutional neural network model to predict diabetes from the image of a retina, comprising:
processing a first dataset and a second dataset, wherein processing the first dataset and the second dataset comprises:
extracting a circular region from a retinal image;
resizing the circular region;
cropping the circular region; and
placing the circular region onto a black background;
training a DenseNet-121 model using an third dataset to yield a first model;
training the first model using the second dataset to yield a second model; and
training the second model using the first dataset to yield a third model,
wherein the third model has an architecture comprising:
a DenseNet-121 backbone;
a global average pooling layer;
a global max pooling layer;
a first composite layer;
a second composite layer; and
a third composite layer.
14 . The method of training a convolutional neural network model to predict diabetes from the image of a retina of claim 13 , wherein the third composite layer comprises:
a batch normalization layer,
a dropout layer; and
a final layer outputting a predictive label corresponding to a diabetic prediction or a non-diabetic prediction.
15 . The method of training a convolutional neural network model to predict diabetes from the image of a retina of claim 13 , wherein the first composite layer comprises:
a batch normalization layer;
a dropout layer;
a linear layer; and
a rectified linear unit layer.