Abstract
An efficient automatic decision support system for detection of retinal disorders is important and is the need of the hour. Optical Coherence Tomography (OCT) is the current imaging modality for the early detection of retinal disorders non-invasively. In this work, a Convolution Neural Network (CNN) model is proposed to classify three types of retinal disorders namely: Choroidal neovascularization (CNV), Drusen macular degeneration (DMD) and Diabetic macular edema (DME). The hyperparameters of the model like batch size, number of epochs, dropout rate, and the type of optimizer are tuned using random search optimization method for better performance to classify different retinal disorders. The proposed architecture provides an accuracy of 97.01%, sensitivity of 93.43%, and 98.07% specificity and it outperformed other existing models, when compared. The proposed model can be used for the large-scale screening of retinal disorders effectively.
Highlights
The eye is the light of human life
The network is trained with 8000 images, where 2000 were trained in each of the four classes
The results show the proposed model has higher efficiency in the classification of Optical Coherence Tomography (OCT) images into disease categories such as Choroidal neovascularization (CNV), Drusen macular degeneration (DMD), Diabetic macular edema (DME), and Normal
Summary
The light enters the eye through the cornea, passing through the aqueous humor, lens, vitreous humor, and on to the retina lying on the back of eye [1]. The retina is the most important part of the eye It is divided into pigment epithelium, receptor layer, cell layer, receptor layer, internal limiting membrane, external limiting membrane, and vitreous body. WHO (2019) estimates that more than 360 million people will be affected worldwide by diabetes mellitus by 2030. All these people will be at risk of developing diabetic macular edema [3]
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