Multilevel deep feature generation framework for automated detection of retinal abnormalities using OCT images

Barua, Prabal Datta and Chan, Wai Yee and Dogan, Sengul and Baygin, Mehmet and Tuncer, Turker and Ciaccio, Edward J. and Islam, Nazrul and Cheong, Kang Hao and Shahid, Zakia Sultana and Acharya, U. Rajendra (2021) Multilevel deep feature generation framework for automated detection of retinal abnormalities using OCT images. Entropy, 23 (12). ISSN 1099-4300, DOI https://doi.org/10.3390/e23121651.

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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.

Item Type: Article
Funders: UNSPECIFIED
Uncontrolled Keywords: OCT image classification;Diabetic Macular Edema (DME); Hybrid deep feature generation;Iterative feature selection; Digital image processing
Subjects: Q Science > QC Physics
R Medicine
R Medicine > R Medicine (General)
R Medicine > R Medicine (General) > Medical technology
R Medicine > RE Ophthalmology
Depositing User: Ms Zaharah Ramly
Date Deposited: 12 Sep 2022 07:20
Last Modified: 12 Sep 2022 07:20
URI: http://eprints.um.edu.my/id/eprint/34327

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