Implementation of the VGG19 Model with Transfer Learning for Retinal Disease Diagnosis: A Study on Normal Eyes, Diabetic Retinopathy, Cataract, and Glaucoma Datasets

Retinal disorders, such as diabetic retinopathy, cataract, and glaucoma, are among the leading causes of vision loss and blindness worldwide. The use of normal data in diagnostic studies provides a basis for distinguishing between pathological and healthy conditions. Complete and accurate diagnosis of these conditions is essential for effective treatment and prevention of recurrence. This study focuses on the VGG19 model and transfer learning to classify retinal conditions such as normal, diabetic, cataract, and glaucoma. A publicly available dataset from Kaggle consisting of labeled retinal images is used for training and evaluation. The data used in this study consists of 400 retinal images, each consisting of 100 images per class, where there are four classes consisting of normal eyes, cataract, diabetic retinopathy and glaucoma. In 50 epochs of training, Adam optimization and softmax function activation, the modeling performance measured using the confusion matrix, including the accuracy, precision, recall and F1 score, achieves accuracy results of 0.91 for 320 training data and 0.88 for 80 validation data. The loss value is 0.18 for the training data and 0.31 for the validation data. Using the test data, the values of the cataract class are 0.94 for precision, 0.8 for recall, and 0.86 for the F1 score. The values are 0.91 for precision, 1.00 for recall and 0.95 for the F1 score in the diabetic retinopathy class. For glaucoma, the scores are 0.74 for precision, 0.85 for recall, and 0.79 for the F1 score. The normal class has scores of 1.00 for precision, 0.9 for recall and 0.95 for the F1 score. Given the performance test results shown above, VGG19 modeling for diagnosing retinal disease provides quite good results. Future research can expand this research by combining additional datasets and exploring other neural network architectures to improve the diagnostic performance.