Artificial Intelligence Hybrid System for Enhancing Retinal Diseases Classification Using Automated Deep Features Extracted from OCT Images

Keywords: OCT, AOCTNet, Deep Learning, Machine Learning, Feature Extraction, Hybrid Systems


After the advent of 2D eye imaging technology, Optical Coherence Tomography (OCT) became one of the most effective and commonly used imaging techniques for non-invasive retinal eye disease evaluation. Blindness is primarily diagnosed using OCT with one of the following two eye diseases categories: diabetic macular edema (DME) or age-related macular degeneration (AMD). The classification of eye retina diseases using OCT images recently became a challenge with the development of machine teaching and profound learning techniques. In this paper, a hybrid artificial intelligence system for multiclass classification of eye retina diseases using automated deep features extracted using Advanced OCT Network (AOCTNet) CNN architecture from OCT images especially spectral domain (SD-OCT) images have been proposed. The proposed methodology mainly can be used to classify retinal diseases into normal and four abnormal classes (AMD, choroidal neovascularization (CNV), DME, and Drusen) retinal disease. The proposed system constructed using eight types of machine learning algorithms, all of which achieved high performance overall. This methodology is a potentially powerful computer aided diagnostic (CAD) tool for the use of SD-OCT imaging for retinal diseases.


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How to Cite
A. Alqudah, A. Alqudah, and M. AlTantawi, “Artificial Intelligence Hybrid System for Enhancing Retinal Diseases Classification Using Automated Deep Features Extracted from OCT Images”, IJISAE, vol. 9, no. 3, pp. 91-100, Sep. 2021.
Research Article