Automatic Classification of Macular Diseases from OCT Images Using CNN Guided with Edge Convolutional Layer

Abstract

Optical Coherence Tomography (OCT) is a non-invasive imaging technology that is widely applied for the diagnosis of retinal pathologies. In general, the structural information of retinal layers plays an important role in the diagnosis of various eye diseases by ophthalmologists. In this paper, by focusing on this information, we first introduce a new layer called the edge convolutional layer (ECL) to accurately extract the retinal boundaries in different sizes and angles with a much smaller number of parameters than the conventional convolutional layer. Then, using this layer, we propose the ECL-guided convolutional neural network (ECL-CNN) method for the automatic classification of the OCT images. For the assessment of the proposed method, we utilize a publicly available data comprising 45 OCT volumes with 15 age-related macular degeneration (AMD), 15 diabetic macular edema (DME), and 15 normal volumes, captured by using the Heidelberg OCT imaging device. Experimental results demonstrate that the suggested ECL-CNN approach has an outstanding performance in OCT image classification, which achieves an average precision of 99.43% as a three-class classification work. Clinical Relevance - The objective of this research is to introduce a new approach based on CNN for the automated classification of retinal OCT images. Clinically, the ophthalmologists should manually check each cross-sectional B-scan and classify retinal pathologies from B-scan images. This manual process is tedious and time-consuming in general. Hence, an automatic computer-assisted technique for retinal OCT image classification is demanded.