Myopia Identification by Fundus Photo Image Classification Using Convolutional Neural Network - Laksono, Giffari Ilham; Winarno, Sri (Politeknik Negeri Batam, 2025)
Myopia is a significant vision problem worldwide, requiring early detection to prevent further damage. This study aims to develop an image classification model using a Convolutional Neural Network (CNN) to identify myopia based on fundus images. The dataset used was 124,749 fundus images, divided into 80% for training and 20% for testing. The applied architecture was EfficientNetB0, chosen for its ability to achieve high performance with efficient computation. Experimental results showed that this model successfully achieved a classification accuracy of 97% in distinguishing between myopic and non-myopic images. These findings demonstrate the potential of CNN, especially EfficientNetB0, as a diagnostic tool for automatic myopia identification, which can accelerate the detection process and improve the accuracy of clinical diagnosis.