A Novel Optimizer in Deep Neural Network for Diabetic Retinopathy Classification

In severe cases, diabetic retinopathy can lead to blindness. For decades, automatic classification of diabetic retinopathy images has been a challenge. Medical image processing has benefited from advances in deep learning systems. To enhance the accuracy of image classification driven by Convolutional Neural Network (CNN), balanced dataset is generated by data augmentation method followed by an optimized algorithm. Deep neural networks (DNN) are frequently optimized using gradient (GD) based techniques. Vanishing gradient is the main drawback of GD algorithms. In this paper, we suggest an innovative algorithm, to solve the above problem, Hypergradient Descent learning rate based Quasi hyperbolic (HDQH) gradient descent to optimize the weights and biases. The algorithms only use first order gradients, which reduces computation time and storage space requirements. The algorithms do not require more tuning of the learning rates as the learning rate tunes itself by means of gradients. We present empirical evaluation of our algorithm on two public retinal image datasets such as Messidor and DDR by using Resnet18 and Inception V3 architectures. The findings of the experiment show that the efficiency and accuracy of our algorithm outperforms the other cutting-edge algorithms. HDQHAdam shows the highest accuracy of 97.5 on Resnet18 and 95.7 on Inception V3 models respectively.