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

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Abstract

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 of 83,484 training images, 41,741 validation images, and 968 test images. According to the experimental results, MIDNet18 obtains an accuracy of 98.86%, and their performances are compared with other standard CNN models; ResNet-50 (83.26%), MobileNet (93.29%) and DenseNet (92.5%). Also, MIDNet18 with a p-value < 0.001 has been proved to be statistically significant than other standard CNN architectures in classifying retinal diseases using OCT images.