A Comparative Study of Deep Learning Parameters for Arcus Senilis Classification

Abstract

Deep learning technique has recently yielded positive results that have increased productivity system for artificial intelligent task, especially the digital image processing and advance machine vision. The popularity of deep learning technique  has a major impact on solving complex problems in many fields, particularly the medical science owing to its applications in medical imaging, disease diagnosis, and much more. However, successful application of deep learning depends upon the appropriate setting of the parameters to achieve better result. Therefore, this paper presents a comparative analysis of different base learning rate and batch size configurations for arcus senilis (AS) classification using deep learning techniques. In this analytical study, a dataset of 402 eye images comprising 158 normal and 244 abnormal eye images was employed. Two well-known ResNet-50, VGG-19 and pre-trained convolutional neural network (CNN) models have been trained and validated with 10-fold cross validation using the proposed dataset. Furthermore, base learning rate and batch size were adjusted accordingly to determine the optimal convergence of each model by observing the validation accuracy and error. Experimental result shows that the best combined system has achieved an overall accuracy of 99.78% with a base learning rate of 0.0001 and a batch size of 20 on CNN pre-trained model validation set. Moreover, CNN produces the best result on F1-score and standard deviation of 99.77% and 0.464 respectively. Thus, it can be concluded that CNN requires a considerably smaller number of parameters and reasonable computing time to achieve state-of-the-art performances. This study shows that CNN has the tendency to consistently improve inaccuracy with growing number of epochs, with no signs of overfitting and performance.