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Nips 2016 tutorial: generative adversarial networks. arXiv:1701.00160. 2016."},{"key":"ref10","doi-asserted-by":"crossref","first-page":"39255","DOI":"10.1007\/s11042-023-14970-5","article-title":"Deep learning based computer-aided automatic prediction and grading system for diabetic retinopathy","volume":"82","author":"Khanna","year":"2023","journal-title":"Multimed Tools Appl"},{"key":"ref11","first-page":"100134","article-title":"Deep learning in computer vision: a critical review of emerging techniques and application scenarios","volume":"6","author":"Chai","year":"2021","journal-title":"Mach Learn Appl"},{"key":"ref12","doi-asserted-by":"crossref","first-page":"103537","DOI":"10.1016\/j.compbiomed.2019.103537","article-title":"Diabetic retinopathy detection using red lesion localization and convolutional neural networks","volume":"116","author":"Zago","year":"2020","journal-title":"Comput Biol 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deep learning for multi-class imbalanced datasets","volume":"149","author":"Saini","year":"2022","journal-title":"Comput Biol Med"},{"key":"ref17","doi-asserted-by":"crossref","first-page":"120554","DOI":"10.1109\/ACCESS.2023.3327900","article-title":"Ensembled deep convolutional generative adversarial network for grading imbalanced diabetic retinopathy recognition","volume":"11","author":"Naz","year":"2023","journal-title":"IEEE Access"},{"key":"ref18","doi-asserted-by":"crossref","first-page":"124441","DOI":"10.1109\/ACCESS.2023.3330436","article-title":"Segmentation using the IC2T model and classification of diabetic retinopathy using the rock hyrax swarm-based coordination attention mechanism","volume":"11","author":"Jagadesh","year":"2023","journal-title":"IEEE Access"},{"key":"ref19","doi-asserted-by":"crossref","first-page":"38299","DOI":"10.1109\/ACCESS.2022.3165193","article-title":"Automatic severity classification of diabetic retinopathy based on denseNet and convolutional block attention module","volume":"10","author":"Farg","year":"2022","journal-title":"IEEE Access"},{"key":"ref20","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1109\/JTEHM.2023.3282104","article-title":"A hybrid convolutional neural network model for automatic diabetic retinopathy classification from fundus images","volume":"11","author":"Ali","year":"2023","journal-title":"IEEE J Transl Eng Health Med"},{"key":"ref21","doi-asserted-by":"crossref","first-page":"106501","DOI":"10.1016\/j.bspc.2024.106501","article-title":"Optimizing diabetic retinopathy detection with inception-V4 and dynamic version of snow leopard optimization algorithm","volume":"96","author":"Yang","year":"2024","journal-title":"Biomed Signal Process Control"},{"key":"ref22","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1007\/978-3-031-22042-5_14","author":"Melin","year":"2023","journal-title":"Fuzzy logic and neural networks for hybrid intelligent system design"},{"key":"ref23","article-title":"IoT-Opthom-CAD: IoT-enabled classification system of multiclass retinal eye diseases using dynamic swin transformers and explainable artificial intelligence","volume":"15","author":"AlBalawi","year":"2024","journal-title":"Int J Adv Comput Sci Appl"},{"key":"ref24","doi-asserted-by":"crossref","first-page":"1159","DOI":"10.1007\/s11760-012-0372-7","article-title":"Image denoising based on gaussian\/bilateral filter and its method noise thresholding","volume":"7","author":"Kumar","year":"2013","journal-title":"Signal Image Video Process"},{"key":"ref25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40537-019-0197-0","article-title":"A survey on image data augmentation for deep learning","volume":"6","author":"Shorten","year":"2019","journal-title":"J Big Data"},{"key":"ref26","series-title":"Proceedings ELMAR-2011","first-page":"393","article-title":"Investigation on the effect of a Gaussian Blur in image filtering and segmentation","author":"Gedraite","year":"2011 Sep 14\u201316"},{"key":"ref27","article-title":"OpenCV Flip Image (cv2.flip)","author":"Rosebrock","year":"2021","journal-title":"PyImageSearch"},{"key":"ref28","doi-asserted-by":"crossref","first-page":"105019","DOI":"10.1016\/j.cmpb.2019.105019","article-title":"Automatic diagnosis of fungal keratitis using data augmentation and image fusion with deep convolutional neural network","volume":"187","author":"Liu","year":"2020","journal-title":"Comput Methods Programs Biomed"},{"key":"ref29","series-title":"2020 International Conference on Intelligent Engineering and Management (ICIEM)","first-page":"79","article-title":"Enhancing performance of deep learning models with different data augmentation techniques: a survey","author":"Khosla","year":"2020 Jun 17\u201319"},{"key":"ref30","doi-asserted-by":"crossref","first-page":"29943","DOI":"10.1007\/s11042-022-14165-4","article-title":"Detection of diabetic retinopathy using convolutional neural networks for feature extraction and classification (DRFEC)","volume":"82","author":"Das","year":"2023","journal-title":"Multimed Tools Appl"},{"key":"ref31","first-page":"1","article-title":"Mean and standard deviation features of color histogram using laplacian filter for content-based image retrieval","volume":"34","author":"Petronas","year":"2011","journal-title":"J Theor Appl Inform Technol"},{"key":"ref32","doi-asserted-by":"crossref","first-page":"2770","DOI":"10.3390\/diagnostics13172770","article-title":"Hybrid fusion of high-resolution and ultra-widefield OCTA acquisitions for the automatic diagnosis of diabetic retinopathy","volume":"13","author":"Li","year":"2023","journal-title":"Diagnostics"},{"key":"ref33","doi-asserted-by":"crossref","first-page":"100303","DOI":"10.1016\/j.health.2024.100303","article-title":"An advanced deep neural network for fundus image analysis and enhancing diabetic retinopathy 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machine (LightGBM)","volume":"11","author":"Rufo","year":"2021","journal-title":"Diagnostics"},{"key":"ref38","unstructured":"DiaretDb1 dataset. [cited 2025 Apr 10]. Available from: https:\/\/paperswithcode.com\/dataset\/diaretdb1."},{"key":"ref39","unstructured":"Messidor dataset. [cited 2025 Apr 10]. Available from: https:\/\/paperswithcode.com\/dataset\/messidor-1."},{"key":"ref40","unstructured":"STARE dataset. [cited 2025 Apr 10]. Available from: https:\/\/paperswithcode.com\/dataset\/stare."},{"key":"ref41","unstructured":"OCTA 500 dataset. [cited 2025 Apr 10]. 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