OCT Vision: Retinal Disease Detection Using Deep Learning

Authors: Chhabria, Sharda, Khedkar, Samruddhi, Dongre, Pratham, Jangde, Shashank, Rangari, Shobhit, Selokar, Sahil, Potle, Shwetal

Source: 2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Application (ICAIQSA) Artificial Intelligence and Quantum Computation-Based Sensor Application (ICAIQSA), 2024 International Conference on. :1-6 Dec, 2024

Relation: 2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Application (ICAIQSA)

Segmentation Based Classification of Retinal Diseases in OCT Images

Authors: Eren, Oyku, Tek, F. Boray, Turkan, Yasemin

Source: 2024 9th International Conference on Computer Science and Engineering (UBMK) Computer Science and Engineering (UBMK), 2024 9th International Conference on. :890-895 Oct, 2024

Relation: 2024 9th International Conference on Computer Science and Engineering (UBMK)

Accurate Classification of Tessellated Retinal Disease with Deep Learning Model

Authors: Anvesh, Kachi, Reshmi, Bharati M

Source: 2024 International Conference on Emerging Smart Computing and Informatics (ESCI) Emerging Smart Computing and Informatics (ESCI), 2024 International Conference on. :1-6 Mar, 2024

Relation: 2024 International Conference on Emerging Smart Computing and Informatics (ESCI)

Semi-Supervised Learning with Out-of-Distribution Unlabeled Samples for Retinal Image Classification

Authors: Jia, Lize, Guo, Jia, Zhang, Weihang, Liu, Hanruo, Wang, Ningli, Li, Huiqi

Source: 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI) Biomedical Imaging (ISBI), 2023 IEEE 20th International Symposium on. :1-5 Apr, 2023

Relation: 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI)

CRAT: Advanced transformer-based deep learning algorithms in OCT image classification

Authors: Yang, Mingming ^a, Du, Junhui ^b, Lv, Ruichan ^{a, ⁎}

Source: In Biomedical Signal Processing and Control June 2025 104

Rotation-Oriented Collaborative Self-Supervised Learning for Retinal Disease Diagnosis

Authors: Li, X., Hu, X., Qi, X., Yu, L., Zhao, W., Heng, P., Xing, L.

Source: IEEE Transactions on Medical Imaging IEEE Trans. Med. Imaging Medical Imaging, IEEE Transactions on. 40(9):2284-2294 Sep, 2021

AdaptiveSwin-CNN: Adaptive Swin-CNN Framework with Self-Attention Fusion for Robust Multi-Class Retinal Disease Diagnosis

Authors: Imran Qureshi

Source: AI, Vol 6, Iss 2, p 28 (2025)

Subject Terms: convolution neural network, fundus imaging, multi-class retinal disease classification, multi-scale features fusion, Swin Transformer, Electronic computers. Computer science, QA75.5-76.95

File Description: electronic resource

Relation: https://www.mdpi.com/2673-2688/6/2/28; https://doaj.org/toc/2673-2688

Access URL: https://doaj.org/article/503b82e445874a79a3b651b1af20810d

OCTNet: A Modified Multi-Scale Attention Feature Fusion Network with InceptionV3 for Retinal OCT Image Classification

Authors: Irshad Khalil, Asif Mehmood, Hyunchul Kim, Jungsuk Kim

Source: Mathematics, Vol 12, Iss 19, p 3003 (2024)

Subject Terms: OCTNet, retinal OCT, Choroidal Neovascularization, diabetic macular edema, Drusen, retinal disease classification, Mathematics, QA1-939

File Description: electronic resource

Relation: https://www.mdpi.com/2227-7390/12/19/3003; https://doaj.org/toc/2227-7390

Access URL: https://doaj.org/article/24f6f75687a34803b994ed482fe92ffc

Accuracy improvement for classifying retinal OCT images by diseases using deep learning-based selective denoising approach

Authors: Lantian Hu, Ruixiang Guo, Sifan Li, Jing Cao, Qian Liu

Source: Journal of Innovative Optical Health Sciences, Vol 16, Iss 06 (2023)

Subject Terms: Optical coherence tomography, deep learning, retinal disease classification, Technology, Optics. Light, QC350-467

File Description: electronic resource

Relation: https://doaj.org/toc/1793-5458; https://doaj.org/toc/1793-7205

Access URL: https://doaj.org/article/9dddb15eb0f6440fa34917c9442f6f38

GABNet: global attention block for retinal OCT disease classification

Authors: Xuan Huang, Zhuang Ai, Hui Wang, Chongyang She, Jing Feng, Qihao Wei, Baohai Hao, Yong Tao, Yaping Lu, Fanxin Zeng

Source: Frontiers in Neuroscience, Vol 17 (2023)

Subject Terms: retinal OCT, retinal disease classification, attention mechanism, model visualization, GABNet, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571

File Description: electronic resource

Relation: https://www.frontiersin.org/articles/10.3389/fnins.2023.1143422/full; https://doaj.org/toc/1662-453X

Access URL: https://doaj.org/article/a6f87292f0af4d3eb30789c4654f134b

Multi-Scale-Denoising Residual Convolutional Network for Retinal Disease Classification Using OCT

Authors: Jinbo Peng, Jinling Lu, Junjie Zhuo, Pengcheng Li

Source: Sensors, Vol 24, Iss 1, p 150 (2023)

Subject Terms: convolutional neural network, retinal disease classification, optical coherence tomography (OCT), multi-scale-denoising residual convolutional network, Chemical technology, TP1-1185

File Description: electronic resource

Relation: https://www.mdpi.com/1424-8220/24/1/150; https://doaj.org/toc/1424-8220

Access URL: https://doaj.org/article/ab521e92dcb848678bd209ac92f2108c

MultiEYE: Dataset and Benchmark for OCT-Enhanced Retinal Disease Recognition from Fundus Images

Authors: Wang, L., Qi, C., Ou, C., An, L., Jin, M., Kong, X., Li, X.

Source: IEEE Transactions on Medical Imaging IEEE Trans. Med. Imaging Medical Imaging, IEEE Transactions on. PP(99):1-1