Dianet: a deep learning based architecture to diagnose diabetes using retinal images only
AssigneeHamad Bin Khalifa University
InventorTanvir Alam
Life of this patent
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Abstract
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.
Claims15
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.
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.
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.
Dependencies are inferred from the claim text.