A novel deep learning technique for analysis and detection of ARMD using OCT scan images

Article type: Research Article

Authors: Prasad Reddy, P.V.G.D.

Affiliations: Department of CS and SE, Andhra University, Visakhapatnam AP, India | E-mail: [email protected]

Correspondence: [*] Corresponding author: Department of CS and SE, Andhra University, Visakhapatnam AP, India. E-mail: [email protected].

Abstract: Age-Related Macular Degeneration (ARMD) is a medical situation resulting in blurred or no vision in the middle of the eye view. Though this disease doesn’t make the person completely blind, it makes it very difficult for the person to perform day to day activities like reading, driving, recognizing people etc. This paper aims to detect ARMD though Optical Coherence Tomography (OCT) scans where the drusen in the macula is detected and identify the infected. The images are first passed though Directional Total Variation (DTV) Denoising followed by Active contour algorithm to mark the boundaries of the layers in macula. In deep learning, a convolutional neural network is a class of deep neural networks, most commonly applied to analyzing visual imagery. Then these images categorized as healthy and infected using Convolution Neural Network. Different CNN variant algorithms like Alexnet, VggNet and GoogleNet have been compared in the experiments and the results obtained are better compared to traditional methods.

Keywords: Age related macular degeneration, optical coherence tomography, directional total variation denoising, active contour, convolution neural network

DOI: 10.3233/KES-210076

Journal: International Journal of Knowledge-based and Intelligent Engineering Systems, vol. 25, no. 3, pp. 335-342, 2021