Multi-Stage Classification of Retinal OCT Using Multi-Scale Ensemble Deep Architecture

Author:

Akinniyi Oluwatunmise1ORCID,Rahman Md Mahmudur1ORCID,Sandhu Harpal Singh2ORCID,El-Baz Ayman2ORCID,Khalifa Fahmi34ORCID

Affiliation:

1. Department of Computer Science, School of Computer, Mathematical and Natural Sciences, Morgan State University, Baltimore, MD 21251, USA

2. Bioengineering Department, University of Louisville, Louisville, KY 20292, USA

3. Electronics and Communications Engineering Department, Mansoura University, Mansoura 35516, Egypt

4. Electrical and Computer Engineering Department, Morgan State University, Baltimore MD 21251, USA

Abstract

Accurate noninvasive diagnosis of retinal disorders is required for appropriate treatment or precision medicine. This work proposes a multi-stage classification network built on a multi-scale (pyramidal) feature ensemble architecture for retinal image classification using optical coherence tomography (OCT) images. First, a scale-adaptive neural network is developed to produce multi-scale inputs for feature extraction and ensemble learning. The larger input sizes yield more global information, while the smaller input sizes focus on local details. Then, a feature-rich pyramidal architecture is designed to extract multi-scale features as inputs using DenseNet as the backbone. The advantage of the hierarchical structure is that it allows the system to extract multi-scale, information-rich features for the accurate classification of retinal disorders. Evaluation on two public OCT datasets containing normal and abnormal retinas (e.g., diabetic macular edema (DME), choroidal neovascularization (CNV), age-related macular degeneration (AMD), and Drusen) and comparison against recent networks demonstrates the advantages of the proposed architecture’s ability to produce feature-rich classification with average accuracy of 97.78%, 96.83%, and 94.26% for the first (binary) stage, second (three-class) stage, and all-at-once (four-class) classification, respectively, using cross-validation experiments using the first dataset. In the second dataset, our system showed an overall accuracy, sensitivity, and specificity of 99.69%, 99.71%, and 99.87%, respectively. Overall, the tangible advantages of the proposed network for enhanced feature learning might be used in various medical image classification tasks where scale-invariant features are crucial for precise diagnosis.

Funder

Center for Equitable Artificial Intelligence and Machine Learning Systems

American Society for Engineering Education

Publisher

MDPI AG

Subject

Bioengineering

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