Research Article
Year 2022,
Volume: 17 Issue: 2, 417 - 426, 30.09.2022
Abstract
References
- Miranda, M., & Romero, F. J. (2019). Antioxidants and Retinal Diseases. Antioxidants, 8(12), 604.
- Berrimi, M., & Moussaoui, A. (2020, October). Deep learning for identifying and classifying retinal diseases. In 2020 2nd International Conference on Computer and Information Sciences (ICCIS) (pp. 1-6). IEEE.
- Sunija, A. P., Kar, S., Gayathri, S., Gopi, V. P., & Palanisamy, P. (2021). Octnet: A lightweight cnn for retinal disease classification from optical coherence tomography images. Computer methods and programs in biomedicine, 200, 105877.
- Abidalkareem, A. J., Abd, M. A., Ibrahim, A. K., Zhuang, H., Altaher, A. S., & Ali, A. M. (2020, July). Diabetic retinopathy (DR) severity level classification using multimodel convolutional neural networks. In 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (pp. 1404-1407). IEEE.
- Wang, J., Hormel, T. T., Gao, L., Zang, P., Guo, Y., Wang, X., ... & Jia, Y. (2020). Automated diagnosis and segmentation of choroidal neovascularization in OCT angiography using deep learning. Biomedical Optics Express, 11(2), 927-944.
- Sun, Y., Li, S., & Sun, Z. (2017). Fully automated macular pathology detection in retina optical coherence tomography images using sparse coding and dictionary learning. Journal of biomedical optics, 22(1), 016012.
- Wang, Y., Zhang, Y., Yao, Z., Zhao, R., & Zhou, F. (2016). Machine learning based detection of age-related macular degeneration (AMD) and diabetic macular edema (DME) from optical coherence tomography (OCT) images. Biomedical optics express, 7(12), 4928-4940.
- Rasti, R., Rabbani, H., Mehridehnavi, A., & Hajizadeh, F. (2017). Macular OCT classification using a multi-scale convolutional neural network ensemble. IEEE transactions on medical imaging, 37(4), 1024-1034.
- Li, F., Chen, H., Liu, Z., Zhang, X., & Wu, Z. (2019). Fully automated detection of retinal disorders by image-based deep learning. Graefe's Archive for Clinical and Experimental Ophthalmology, 257(3), 495-505.
- Mishra, S. S., Mandal, B., & Puhan, N. B. (2019). Multi-level dual-attention based CNN for macular optical coherence tomography classification. IEEE Signal Processing Letters, 26(12), 1793-1797.
- Motozawa, N., An, G., Takagi, S., Kitahata, S., Mandai, M., Hirami, Y., ... & Kurimoto, Y. (2019). Optical coherence tomography-based deep-learning models for classifying normal and age-related macular degeneration and exudative and non-exudative age-related macular degeneration changes. Ophthalmology and therapy, 8(4), 527-539.
- Najeeb, S., Sharmile, N., Khan, M. S., Sahin, I., Islam, M. T., & Bhuiyan, M. I. H. (2018, December). Classification of retinal diseases from OCT scans using convolutional neural networks. In 2018 10th International Conference on Electrical and Computer Engineering (ICECE) (pp. 465-468). IEEE.
- Wang, W., Xu, Z., Yu, W., Zhao, J., Yang, J., He, F., ... & Li, X. (2019, October). Two-stream CNN with loose pair training for multi-modal AMD categorization. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 156-164). Springer, Cham.
- Kermany D, Zhang K, Goldbaum M, 2018, Large Dataset of Labeled Optical Coherence Tomography (Oct) and Chest X-Ray Images, Mendeley Data.
- Deng, L., & Yu, D. (2014). Deep learning: methods and applications. Foundations and trends in signal processing, 7(3–4), 197-387.
- ŞAHİN, M. E., ULUTAŞ, H., & Esra, Y. Ü. C. E. A deep learning approach for detecting pneumonia in chest X-rays. Avrupa Bilim ve Teknoloji Dergisi, (28), 562-567.
- Lo, S. C., Lou, S. L., Lin, J. S., Freedman, M. T., Chien, M. V., & Mun, S. K. (1995). Artificial convolution neural network techniques and
A Deep Learning-Based Technique for Diagnosing Retinal Disease by Using Optical Coherence Tomography (OCT) Images
Abstract
The retina layer is the most complex and sensitive part of the eye, and disorders that affect it have a big impact on people's lives. The Optical Coherence Tomography (OCT) imaging technology can be used to diagnose diseases that are caused by pathological alterations in the retina. The importance of early diagnosis in the management of these illnesses cannot be overstated. In this article, an approach based on convolutional neural networks (CNN), a deep learning method, is presented for the detection of retinal disorders from OCT images. A new CNN architecture has been developed for disease diagnosis and classification. The proposed method has been found to have an accuracy rate of 94% in the detection of retinal disorders. The results are obtained by comparing the proposed CNN network model in a deep learning application used in classification with the MobileNet50 network model in the literature. The evaluation parameter values for models trained using the 5-fold cross validation approach for each type of disease in the retinal OCT image dataset are also submitted. The proposed method can clearly be utilized as a decision-making tool to assist clinicians in diagnosing retinal illnesses in a clinical context based on its effectiveness thus far.
Keywords
References
- Miranda, M., & Romero, F. J. (2019). Antioxidants and Retinal Diseases. Antioxidants, 8(12), 604.
- Berrimi, M., & Moussaoui, A. (2020, October). Deep learning for identifying and classifying retinal diseases. In 2020 2nd International Conference on Computer and Information Sciences (ICCIS) (pp. 1-6). IEEE.
- Sunija, A. P., Kar, S., Gayathri, S., Gopi, V. P., & Palanisamy, P. (2021). Octnet: A lightweight cnn for retinal disease classification from optical coherence tomography images. Computer methods and programs in biomedicine, 200, 105877.
- Abidalkareem, A. J., Abd, M. A., Ibrahim, A. K., Zhuang, H., Altaher, A. S., & Ali, A. M. (2020, July). Diabetic retinopathy (DR) severity level classification using multimodel convolutional neural networks. In 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (pp. 1404-1407). IEEE.
- Wang, J., Hormel, T. T., Gao, L., Zang, P., Guo, Y., Wang, X., ... & Jia, Y. (2020). Automated diagnosis and segmentation of choroidal neovascularization in OCT angiography using deep learning. Biomedical Optics Express, 11(2), 927-944.
- Sun, Y., Li, S., & Sun, Z. (2017). Fully automated macular pathology detection in retina optical coherence tomography images using sparse coding and dictionary learning. Journal of biomedical optics, 22(1), 016012.
- Wang, Y., Zhang, Y., Yao, Z., Zhao, R., & Zhou, F. (2016). Machine learning based detection of age-related macular degeneration (AMD) and diabetic macular edema (DME) from optical coherence tomography (OCT) images. Biomedical optics express, 7(12), 4928-4940.
- Rasti, R., Rabbani, H., Mehridehnavi, A., & Hajizadeh, F. (2017). Macular OCT classification using a multi-scale convolutional neural network ensemble. IEEE transactions on medical imaging, 37(4), 1024-1034.
- Li, F., Chen, H., Liu, Z., Zhang, X., & Wu, Z. (2019). Fully automated detection of retinal disorders by image-based deep learning. Graefe's Archive for Clinical and Experimental Ophthalmology, 257(3), 495-505.
- Mishra, S. S., Mandal, B., & Puhan, N. B. (2019). Multi-level dual-attention based CNN for macular optical coherence tomography classification. IEEE Signal Processing Letters, 26(12), 1793-1797.
- Motozawa, N., An, G., Takagi, S., Kitahata, S., Mandai, M., Hirami, Y., ... & Kurimoto, Y. (2019). Optical coherence tomography-based deep-learning models for classifying normal and age-related macular degeneration and exudative and non-exudative age-related macular degeneration changes. Ophthalmology and therapy, 8(4), 527-539.
- Najeeb, S., Sharmile, N., Khan, M. S., Sahin, I., Islam, M. T., & Bhuiyan, M. I. H. (2018, December). Classification of retinal diseases from OCT scans using convolutional neural networks. In 2018 10th International Conference on Electrical and Computer Engineering (ICECE) (pp. 465-468). IEEE.
- Wang, W., Xu, Z., Yu, W., Zhao, J., Yang, J., He, F., ... & Li, X. (2019, October). Two-stream CNN with loose pair training for multi-modal AMD categorization. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 156-164). Springer, Cham.
- Kermany D, Zhang K, Goldbaum M, 2018, Large Dataset of Labeled Optical Coherence Tomography (Oct) and Chest X-Ray Images, Mendeley Data.
- Deng, L., & Yu, D. (2014). Deep learning: methods and applications. Foundations and trends in signal processing, 7(3–4), 197-387.
- ŞAHİN, M. E., ULUTAŞ, H., & Esra, Y. Ü. C. E. A deep learning approach for detecting pneumonia in chest X-rays. Avrupa Bilim ve Teknoloji Dergisi, (28), 562-567.
- Lo, S. C., Lou, S. L., Lin, J. S., Freedman, M. T., Chien, M. V., & Mun, S. K. (1995). Artificial convolution neural network techniques and
There are 17 citations in total.
Details
| Primary Language | English |
|---|---|
| Subjects | Engineering |
| Journal Section | TJST |
| Authors | |
| Publication Date | September 30, 2022 |
| Submission Date | June 9, 2022 |
| Published in Issue | Year 2022 Volume: 17 Issue: 2 |
