Abstract
•This paper proposes the application of deep learning algorithms for diagnosing 14 major ophthalmological defects such as Hollenhorst Emboli, Arteriosclerotic Retinopathy etc.•In this study, multiple performance evaluation techniques such as Precision, Recall, F-1 Score, etc. are used to compare deep learning algorithms.•In this study, the performance compared with the existing literature achieved higher accuracy due to the unique model and its configuration, hyperparameter tuning and pre-processing techniques for the 14 classes of retinal defects.
Retina is the heart of an eye which generates electrical impulses due to light sensitivity. The vessel formation in human eye is an essential key for diagnosing ophthalmological conditions. This paper aims to diagnose ophthalmological conditions through deep learning models and provide advancements in early detection of ophthalmological conditions for proper treatment to protect patient’s vision, and for health care giver worldwide. STARE dataset is used for this study which consists over 385 retinal images of 14 ophthalmological defects such as BRAO, CRAO, etc. This dataset is further pre-processed over the techniques such as augmentation, normalization, etc for obtaining the best refined features for training deep learning algorithms. This paper broadly implements 5 deep learning algorithms i.e., EfficientNet, 3-Layers CNN, InceptionV2, ResNet-50, VGG-16. These models are trained number of times over tuned hyperparameters such as batch size etc and evaluated over 4 performance metrics over weighted averaged and macro averaged of precision, recall, F1-score, and accuracy to acquire the best performing model. EfficientNet performed the best with 98.43% accuracy, macro averaged f-1 score, recall, precision as 98.37%, 99.16%, 97.91% and weighted averaged f-1 score, recall, precision, as 98.50%, 98.43%, 98.82% over batch size 64. In this study, the performance compared with the existing literature achieved higher accuracy due to the unique model and its configuration, hyperparameter tuning and pre-processing techniques for the 14 classes of retinal defects. The future work includes classifying more ophthalmological conditions, adding more parameters from blood, etc.