Ret-Detect: Deep Learning-Driven Automated Detection of Retinal Diseases

Visible light from the environment is captured by human eyes, and the retina creates an image of it and sends it to the human brain. However, this process gets interrupted when our eyes get affected by diseases. Retinal diseases, including AMD, DR, and Glaucoma, are heavily responsible for vision impairment and blindness. To get rid of them and prevent loss of our vision, we need to diagnose these diseases early. Sometimes it becomes difficult to move to a medical center and manage a trained doctor to diagnose on time. In order to solve this issue, we can take advantage of deep learning-based machine learning. This paper proposes a CNN-based deep learning model named "Ret-Detect" to detect retinal diseases using retinal images with a higher success rate. In this model, we use a total of 39,800 retinal images, and we split them for training and testing. We use 80% of the total image, which is 32000 images to train the model, 20% of the images, totaling 7,800 images, will be used for testing purposes. Our model got a training accuracy of 97.96%, and a validation accuracy of 96.00%. We also made a comparison with some pre-trained models, such as MobileNet, DenseNet121, VGG19, InceptionV3, VGG16, ResNet50 and Ensemble model and we got an accuracy of 86.10%, 75.59%, 92.62%, 94.35%, 23.75%, 73.62% and 94.76%for validation. We observed that our custom model shows a better accuracy to detect retinal diseases, and it can provide a noticeable assistance to reduce the number of blindness all over the world.