Research Article
Ensemble Framework of Deep CNNs for Diabetic Retinopathy Detection
| Require: fundus images (X,Y); where Y = {y/y ϵ {normal, mild, moderate, severe, PDR}} | | Output: the trained model that classifies the fundus images x ϵ X | (1) | Perform preprocessing: | (i) | Resize the image to dimension 786 × 512 | (ii) | Perform augmentation: randomly crop five patches, of size 512 × 512, of each image and perform flip flop and 90 degree rotation | (iii) | Mean normalize the each image | | Import a set of pretrained models = {Dense121, ResNet50, Inception-V3} | | Replace the last fully connected layer of each model by a layer of (5 × 1) dimension | | foreach ∀h ϵ do | | α = 0.0001 | | for epochs = 1 to 50 do | | foreach mini batch(Xi,Yi) ϵ (Xtrain, Ytrain) do | | Update the parameters of the model h(.) using Eq.2 | | if the validation error is not improving for five epochs then | | stop training | | end | | end | | end | | end | | foreach x ϵ Xtest do | | Ensemble the output of all models, h ϵ, using equation (3) | | End |
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