1. Comparison of the proposed DCNN model with standard CNN architectures for retinal diseases classification

    Comparison of the proposed DCNN model with standard CNN architectures for retinal diseases classification

    Deep learning in medical image analysis has indicated increasing interest in the classification of signs of abnormalities. In this study, a new convolutional neural network (CNN) architecture (MIDNet18) Medical Image Detection Network was proposed for the classification of retinal diseases using optical coherence tomography (OCT) images. The model consists of 14 convolutional layers, seven Max Pooling layers, four dense layers, and one classification layer. A multi-class classification layer in the MIDNet18 is used to classify the OCT images into either normal or any of the three abnormal types: Choroidal Neovascularization (CNV), Drusen, and Diabetic Macular Edema (DME). The dataset consists ...

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