VGG-19 Transfer Learning Technique for Automated Multi-Class Retinal Disease Detection: Model Development and Validation on a Ghanaian Fundus Image Dataset

Artificial Intelligence is radically transforming various fields including the field of medical diagnosis and imaging especially for Computer-Aided Diagnosis (CAD). Automated disease detection from the retina has become increasingly important, especially in ophthalmology, where the eye offers a non-invasive way of visualizing and monitoring the progression of diseases. Early detection of these diseases is essential for preventing irreversible blindness. Although, various research have been carried out in Ghana in the area of artificial intelligence using convolutional neural network and machine learning, there is gap in literature on artificial intelligence focusing on local retinal fundus images using deep transfer learning techniques in Ghana. This study address the gap by using 184 retinal fundus images for patients between the ages of 10-70 years from Ghana using Artificial Intelligence Deep Transfer Learning (AIDL) techniques with the VGG-19 architecture augmentation to prepare them for training, testing, and validation, employing a deep transfer learning algorithm known as Convolutional Neural Network (CNN) due to the image size. After a two-stage classification approach enabled the distinction between healthy and unhealthy retinal images, and subsequently, classifying diverse retinal conditions from the unhealthy images including glaucoma, hypertensive and diabetic retinopathy, as well as chorio retinal and macular changes. The performance of the proposed solution was evaluated using various metrics such as accuracy, precision, recall, and AUC for the binary classification and the deep learning task. The results showed that, the proposed solution achieved high accuracy of 97.31%, precision of 96.85%, recall of 98.06%, and AUC of 0.993. This demonstrates the effectiveness in detecting various retina diseases. This solution enhance significant potential automated retinal disease screening, early diagnosis and tele optometry support services, contributing to the eradication of irreversible blindness especially for low resource communities in Ghana and Africa at large.