# Classification of dry age-related macular degeneration and diabetic macular edema from optical coherence tomography images using dictionary learning

## TL;DR

This paper presents a novel dictionary learning-based classification method using HOG features for early detection of AMD and DME from OCT images, achieving high accuracy without retinal layer segmentation.

## Contribution

It introduces a segmentation-free approach utilizing HOG features and dictionary learning to classify AMD, DME, and normal OCT images with high accuracy, especially in early disease stages.

## Key findings

- Achieved 95.13% accuracy for DME detection.
- Achieved 100% accuracy for AMD and normal classification.
- Outperformed existing methods by using only 4% of B-Scans.

## Abstract

Age-related Macular Degeneration (AMD) and Diabetic Macular Edema (DME) are the major causes of vision loss in developed countries. Alteration of retinal layer structure and appearance of exudate are the most significant signs of these diseases. With the aim of automatic classification of DME, AMD and normal subjects from Optical Coherence Tomography (OCT) images, we proposed a classification algorithm. The two important issues intended in this approach are, not utilizing retinal layer segmentation which by itself is a challenging task and attempting to identify diseases in their early stages, where the signs of diseases appear in a small fraction of B-Scans. We used a histogram of oriented gradients (HOG) feature descriptor to well characterize the distribution of local intensity gradients and edge directions. In order to capture the structure of extracted features, we employed different dictionary learning-based classifiers. Our dataset consists of 45 subjects: 15 patients with AMD, 15 patients with DME and 15 normal subjects. The proposed classifier leads to an accuracy of 95.13%, 100.00%, and 100.00% for DME, AMD, and normal OCT images, respectively, only by considering the 4% of all B-Scans of a volume which outperforms the state of the art methods.