Dry and Wet Age-Related Macular Degeneration Classification Using Oct Images and Deep Learning
DOI:
https://doi.org/10.52783/jns.v14.2217Keywords:
AMD Classification, OCT Image Analysis, Vision Transformers, Deep Learning, Hugging Face Transformers, PyTorch, Retinal Disease DetectionAbstract
AMD represents a substantial reason behind vision loss because it exists as two different types known as Dry and Wet AMD thus requiring swift proper diagnosis methods to deliver successful treatment possibilities. The research examines the application of Vision Transformers (ViTs) in Global OCT Image Analysis through their ability to detect local and global retinal abnormalities using the self-attention mechanism. The proposed model operates through Hugging Face Transformers and PyTorch to analyze high-resolution OCT images for precise identification of Dry and Wet AMD. The classification accuracy together with robustness is superior in ViTs compared to standard CNN-based approaches when analyzing constrained datasets. The produced results show substantial improvement of diagnostic accuracy together with better feature capturing abilities and more transparent assessment capabilities making automated AMD diagnosis more efficient. The research proves that ViTs represent an advanced deep learning technique which can deliver optimized retinal disease classification from OCT images while offering an effective AI-assisted diagnostic system. The subsequent research will focus on combining multiple data sources to boost practical medical system use.
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Y. Li, J. Hu, X. Zhang, and X. Zhu, "Automated and Fast Classification of Dry and Wet Age-Related Macular Degeneration Using Deep Convolutional Neural Networks," IEEE Access, vol. 7, pp. 136929–136938, 2019.
C. S. Lee, D. M. Baughman, and A. Y. Lee, "Deep Learning Is Effective for the Classification of OCT Images of Normal Versus Age-Related Macular Degeneration," arXiv preprint arXiv:1612.04891, 2016.
W. Wang et al., "Learning Two-Stream CNN for Multi-Modal Age-Related Macular Degeneration Categorization," arXiv preprint arXiv:2012.01879, 2020.
S. Sotoudeh-Paima, A. Jodeiri, F. Hajizadeh, and H. Soltanian-Zadeh, "Multi-Scale Convolutional Neural Network for Automated AMD Classification Using Retinal OCT Images," arXiv preprint arXiv:2110.03002, 2021.
A. M. Hagag et al., "Deep-Learning-Based Clustering of OCT Images for Biomarker Discovery in Age-Related Macular Degeneration," arXiv preprint arXiv:2405.09549, 2024.
M. Wu et al., "Classification of Dry and Wet Macular Degeneration Based on the ConvNeXT Model," Frontiers in Computational Neuroscience, vol. 16, 2022.
J. Kim et al., "Retinal Disease Classification from OCT Images Using Deep Learning," IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), 2021.
A. Serener and S. Serte, "Dry and Wet Age-Related Macular Degeneration Classification Using OCT Images and Deep Learning," 2019 Medical Technologies Congress (TIPTEKNO), 2019.
A. M. Hagag et al., "Deep-Learning-Based Clustering of OCT Images for Biomarker Discovery in Age-Related Macular Degeneration," arXiv preprint arXiv:2405.09549, 2024.
C. S. Lee, D. M. Baughman, and A. Y. Lee, "Deep Learning Is Effective for the Classification of OCT Images of Normal Versus Age-Related Macular Degeneration," arXiv preprint arXiv:1612.04891, 2016.
W. Wang et al., "Learning Two-Stream CNN for Multi-Modal Age-Related Macular Degeneration Categorization," arXiv preprint arXiv:2012.01879, 2020.
S. Sotoudeh-Paima et al., "Multi-Scale Convolutional Neural Network for Automated AMD Classification Using Retinal OCT Images," arXiv preprint arXiv:2110.03002, 2021.
Y. Li et al., "Automated and Fast Classification of Dry and Wet Age-Related Macular Degeneration Using Deep Convolutional Neural Networks," IEEE Access, vol. 7, pp. 136929–136938, 2019.
A. M. Hagag et al., "Deep-Learning-Based Clustering of OCT Images for Biomarker Discovery in Age-Related Macular Degeneration," arXiv preprint arXiv:2405.09549, 2024.
C. S. Lee, D. M. Baughman, and A. Y. Lee, "Deep Learning Is Effective for the Classification of OCT Images of Normal Versus Age-Related Macular Degeneration," arXiv preprint arXiv:1612.04891, 2016.
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