Multilevel Deep Feature Generation Framework for Automated Detection of Retinal Abnormalities Using OCT Images

Prabal Datta Barua; Wai Yee Chan; Sengul Dogan; Mehmet Baygin; Turker Tuncer; Edward J Ciaccio; Nazrul Islam; Kang Hao Cheong; Zakia Sultana Shahid; U Rajendra Acharya

doi:10.3390/e23121651

Multilevel Deep Feature Generation Framework for Automated Detection of Retinal Abnormalities Using OCT Images

Entropy (Basel). 2021 Dec 8;23(12):1651. doi: 10.3390/e23121651.

Authors

Prabal Datta Barua^{1

2

3}, Wai Yee Chan⁴, Sengul Dogan⁵, Mehmet Baygin⁶, Turker Tuncer⁵, Edward J Ciaccio⁷, Nazrul Islam⁸, Kang Hao Cheong⁹, Zakia Sultana Shahid¹⁰, U Rajendra Acharya^{11

12

13}

Affiliations

¹ School of Management & Enterprise, University of Southern Queensland, Toowoomba, QLD 4350, Australia.
² Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia.
³ Cogninet Brain Team, Cogninet Australia, Sydney, NSW 2010, Australia.
⁴ University Malaya Research Imaging Centre, Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Kuala Lumpur 59100, Malaysia.
⁵ Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig 23002, Turkey.
⁶ Department of Computer Engineering, College of Engineering, Ardahan University, Ardahan 75000, Turkey.
⁷ Department of Medicine, Columbia University Irving Medical Center, New York, NY 10032-3784, USA.
⁸ Glaucoma Faculty, Bangladesh Eye Hospital & Institute, Dhaka 1206, Bangladesh.
⁹ Science, Mathematics and Technology Cluster, Singapore University of Technology and Design, Singapore 487372, Singapore.
¹⁰ Department of Ophthalmology, Anwer Khan Modern Medical College, Dhaka 1205, Bangladesh.
¹¹ Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore.
¹² Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore 129799, Singapore.
¹³ Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung 41354, Taiwan.

Abstract

Optical coherence tomography (OCT) images coupled with many learning techniques have been developed to diagnose retinal disorders. This work aims to develop a novel framework for extracting deep features from 18 pre-trained convolutional neural networks (CNN) and to attain high performance using OCT images. In this work, we have developed a new framework for automated detection of retinal disorders using transfer learning. This model consists of three phases: deep fused and multilevel feature extraction, using 18 pre-trained networks and tent maximal pooling, feature selection with ReliefF, and classification using the optimized classifier. The novelty of this proposed framework is the feature generation using widely used CNNs and to select the most suitable features for classification. The extracted features using our proposed intelligent feature extractor are fed to iterative ReliefF (IRF) to automatically select the best feature vector. The quadratic support vector machine (QSVM) is utilized as a classifier in this work. We have developed our model using two public OCT image datasets, and they are named database 1 (DB1) and database 2 (DB2). The proposed framework can attain 97.40% and 100% classification accuracies using the two OCT datasets, DB1 and DB2, respectively. These results illustrate the success of our model.

Keywords: OCT image classification; diabetic macular edema (DME); digital image processing; hybrid deep feature generation; iterative feature selection.