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Classification of Retinal Pathology via OCT Images using Convolutional Neural Network

Optical Coherence Tomography (OCT) is a medical imaging technique used to detect pathology that occurs in the macula. The manual analysis process tends to be less effective and efficient both in time and diagnostic accuracy. This study proposes an automatic classification system for generalized macular retinal pathology based on OCT retinal images using Convolutional Neural Network (CNN) with EfficientNet architecture. In the preprocessing stage, three types of signal processing are analyzed on the image, namely Gaussian Filter, Contrast Limited Adaptive Histogram Equalization (CLAHE), and Gabor Filter. This paper also evaluates two different optimizers, namely Adaptive Moment (Adam) and Stochastic Gradient Decent (SGD). The EfficientNet model works using a combined scaling method to balance all network dimensions. The experimental results show that the proposed model's preprocessing CLAHE and Adam's optimization function can classify four standard retinal macular pathology classes. The four classes include Age-Related Macular Degeneration (AMD), Choroidal Neovascularization (CNV), and Diabetic Macular Edema (DME), with an accuracy of 90.60. %, and a loss of 0.27.