Detection and Multiclass Classification of Ocular Diseases using Deep Learning-based Ensemble Model - Gulati, Seema; Guleria, Kalpna; Goyal, Nitin (International Journal of Intelligent Systems and Applications in Engineering, 2024)
The technological advances have made it possible to design and develop automated systems for the detection of diseases which saves time and enables early treatment, fostering good health. A lot of people in developing countries lose their vision to ocular diseases at an early age. To prevent irreversible damage to the eyesight timely detection and treatment are imperative. Nowadays, automatic detection of such vision-threatening diseases is possible with the help of artificial intelligence (AI) systems. Diabetic Mellitus or diabetes is one of the diseases which causes many ocular diseases such as diabetic retinopathy, diabetic macular edema, cataract, and glaucoma. The proposed work automatically detects three such ocular diseases: Choroidal neovascularization, Diabetic macular edema and Drusen. The proposed work uses an ensemble approach to detect ocular diseases wherein three models have been designed. The ensembles have been designed using the feature extractor method of the VGG16, Xception and mobilenet models and then extracted features are given to a convolutional neural network which then trains and classifies the input tomographic images into Choroidal neovascularization, Diabetic macular edema, Drusen and Normal. The proficiency of the ensemble models is assessed using the metrics- prediction accuracy, class-wise accuracy, precision, recall and f1-score. The ensemble model – MobileNet with CNN yields the best results with an average accuracy of 95.34%.