Optimization of the Convolutional Neural Network Method Using Fine-Tuning for Image Classification of Eye Disease

Abstract

Abstract. The eye is the most important organ of the human body which functions as the sense of sight. Most people wish they had healthy eyes so they could see clearly about life around them. However, some people experience eye health problems. There are many types of eye diseases ranging from mild to severe. With advances in technology, artificial intelligence can be used to classify eye diseases accurately, one of which is deep learning. Therefore, this study uses the Convolutional Neural Network (CNN) algorithm to classify eye diseases using the VGG16 architecture as a base model and will be combined using a fine-tuning model as an optimization to improve accuracy. Purpose:To find out the accuracy results obtained in the fine-tuning optimization model on Convolutional Neural Network (CNN) method in classifying images in eye disease. Methods/Study design/approach: Combining the Convolutional Neural Network (CNN) method with fine-tuning optimization models for image classification in eye disease. The two methods will be compared to determine the best result. Result/Findings: The accuracy results obtained from testing the Convolutional Neural Network method with the VGG16 architecture were 82.63% while the accuracy results from testing the fine-tuning model were 94.13%. Novelty/Originality/Value: The test results on the fine-tuning model have better accuracy than the testing of the Convolutional Neural Network method. This can be seen in the fine-tuning model which has an increase in accuracy of 11.5%.

Keywords

• Eye Disease
• Convolutional Neural Network
• Fine-Tuning
• VGG16
• Deep Learning