Retinal diseases classification based on hybrid ensemble deep learning and optical coherence tomography images

Kuntha Pin; Jung Woo Han; Yunyoung Nam; Kuntha Pin; Jung Woo Han; Yunyoung Nam

doi:10.3934/era.2023248

Electronic Research Archive

2023, Volume 31, Issue 8: 4843-4861. doi: 10.3934/era.2023248

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Retinal diseases classification based on hybrid ensemble deep learning and optical coherence tomography images

1.
Department of ICT Convergence, Soonchunhyang University, Asan 31538, Korea
2.
Department of Ophthalmology, Bucheon Hospital, Soonchunhyang University College of Medicine, Bucheon 14584, Korea
3.
Department of Computer Science and Engineering, Soonchunhyang University, Asan 31538, Korea
† These two authors contributed equally.

Received: 01 June 2023 Revised: 17 June 2023 Accepted: 19 June 2023 Published: 07 July 2023

Optical coherence tomography (OCT) is a noninvasive, high-resolution imaging technique widely used in clinical practice to depict the structure of the retina. Over the past few decades, ophthalmologists have used OCT to diagnose, monitor, and treat retinal diseases. However, manual analysis of the complicated retinal layers using two colors, black and white, is time consuming. Although ophthalmologists have more experience, their results may be prone to erroneous diagnoses. Therefore, in this study, we propose an automatic method for diagnosing five retinal diseases based on the use of hybrid and ensemble deep learning (DL) methods. DL extracts a thousand constitutional features from images as features for training classifiers. The machine learning method classifies the extracted features and fuses the outputs of the two classifiers to improve classification performance. The distribution probabilities of two classifiers of the same class are aggregated; then, class prediction is made using the class with the highest probability. The limited dataset is resolved by the fine-tuning of classification knowledge and generating augmented images using transfer learning and data augmentation. Multiple DL models and machine learning classifiers are used to access a suitable model and classifier for the OCT images. The proposed method is trained and evaluated using OCT images collected from a hospital and exhibits a classification accuracy of 97.68% (InceptionResNetV2, ensemble: Extreme gradient boosting (XG-Boost) and k-nearest neighbor (k-NN). The experimental results show that our proposed method can improve the OCT classification performance; moreover, in the case of a limited dataset, the proposed method is critical to develop accurate classifications.
- OCT image,
- retinal disease,
- deep learning,
- machine learning,
- ensemble classifiers,
- hybrid machine learning and deep learning
Citation: Kuntha Pin, Jung Woo Han, Yunyoung Nam. Retinal diseases classification based on hybrid ensemble deep learning and optical coherence tomography images[J]. Electronic Research Archive, 2023, 31(8): 4843-4861. doi: 10.3934/era.2023248

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Abstract

Optical coherence tomography (OCT) is a noninvasive, high-resolution imaging technique widely used in clinical practice to depict the structure of the retina. Over the past few decades, ophthalmologists have used OCT to diagnose, monitor, and treat retinal diseases. However, manual analysis of the complicated retinal layers using two colors, black and white, is time consuming. Although ophthalmologists have more experience, their results may be prone to erroneous diagnoses. Therefore, in this study, we propose an automatic method for diagnosing five retinal diseases based on the use of hybrid and ensemble deep learning (DL) methods. DL extracts a thousand constitutional features from images as features for training classifiers. The machine learning method classifies the extracted features and fuses the outputs of the two classifiers to improve classification performance. The distribution probabilities of two classifiers of the same class are aggregated; then, class prediction is made using the class with the highest probability. The limited dataset is resolved by the fine-tuning of classification knowledge and generating augmented images using transfer learning and data augmentation. Multiple DL models and machine learning classifiers are used to access a suitable model and classifier for the OCT images. The proposed method is trained and evaluated using OCT images collected from a hospital and exhibits a classification accuracy of 97.68% (InceptionResNetV2, ensemble: Extreme gradient boosting (XG-Boost) and k-nearest neighbor (k-NN). The experimental results show that our proposed method can improve the OCT classification performance; moreover, in the case of a limited dataset, the proposed method is critical to develop accurate classifications.

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