Klasifikasi Penyakit Mata pada Citra Fundus Menggunakan VGG-16 - Sutanto, Steven Yesua; Udjulawa, Daniel (Universitas Multi Data Palembang, 2024)
The eye is a vital sensory organ crucial for vision and various aspects of daily life. Eye diseases such as diabetic retinopathy, glaucoma, cataracts, macular degeneration, hypertension, pathological myopia, and other diseases are global health issues that significantly impact quality of life. The 2022 RAAB survey by Perdami revealed that 8 million people in Indonesia suffer from visual impairments, with 1.6 million of them being blind. Diagnosing eye diseases often requires considerable time and depends on the accuracy and subjectivity of doctors analyzing fundus images. Convolutional Neural Network (CNN) methods can process images and recognize complex patterns and features, assisting in the classification of eye diseases with high accuracy and efficiency. This research aims to classify various eye diseases automatically using the CNN method, speeding up the diagnosis process, enabling faster treatment, and improving effectiveness in the medical field. The implementation of the CNN method with the VGG-16 architecture was successful, capable of classifying 8 types of eye diseases, with the best result obtained in the 10th trial, achieving an accuracy of 54.17%