Multilevel deep feature generation framework for automated detection of retinal abnormalities using OCT images
Article
Barua, Prabal Datta, Chan, Wai Yee Chan, Dogan, Sengul, Baygin, Mehmet, Tuncer, Turker, Ciaccio, Edward J., Islam, Nazrul, Cheong, Kang Hao, Shahid, Zakia Sultana and Acharya, Rajendra. 2021. "Multilevel deep feature generation framework for automated detection of retinal abnormalities using OCT images." Entropy: international and interdisciplinary journal of entropy and information studies. 23 (12). https://doi.org/10.3390/e23121651
| Article Title | Multilevel deep feature generation framework for automated detection of retinal abnormalities using OCT images |
|---|---|
| ERA Journal ID | 39951 |
| Article Category | Article |
| Authors | Barua, Prabal Datta, Chan, Wai Yee Chan, Dogan, Sengul, Baygin, Mehmet, Tuncer, Turker, Ciaccio, Edward J., Islam, Nazrul, Cheong, Kang Hao, Shahid, Zakia Sultana and Acharya, Rajendra |
| Journal Title | Entropy: international and interdisciplinary journal of entropy and information studies |
| Journal Citation | 23 (12) |
| Number of Pages | 18 |
| Year | 2021 |
| ISSN | 1099-4300 |
| Digital Object Identifier (DOI) | https://doi.org/10.3390/e23121651 |
| Web Address (URL) | https://www.mdpi.com/1099-4300/23/12/1651 |
| 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. © 2021 by the authors. Licensee MDPI, Basel, Switzerland. |
| Keywords | Diabetic macular edema (DME) |
| ANZSRC Field of Research 2020 | 400306. Computational physiology |
| Public Notes | Export Date: 9 October 2023 |
| Byline Affiliations | School of Management and Enterprise |
| University of Technology Sydney | |
| Cogninet Australia, Australia | |
| University of Malaya, Malaysia | |
| Firat University, Turkey | |
| Ardahan University, Turkiye | |
| Columbia University Irving Medical Center, United States | |
| Bangladesh Eye Hospital and Institute, Bangladesh | |
| Singapore University of Technology and Design | |
| Anwer Khan Modern Medical College, Bangladesh | |
| Ngee Ann Polytechnic, Singapore | |
| Singapore University of Social Sciences (SUSS), Singapore | |
| Asia University, Taiwan |
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