Regions of Interest (ROI) Key Terms Deep Learning Object Recognition Convolutional Neural Network (CNN) Object Detection Custom Vision API Regions of Interest (ROI) Object Classification MS CNTK Detect like a Human Eye? 04 01 02 03 Locate objects? Recognize object /text in an image / video? M...
* 题目: Federated Uncertainty-Aware Aggregation for Fundus Diabetic Retinopathy Staging* PDF: arxiv.org/abs/2303.1303* 作者: Meng Wang,Lianyu Wang,Xinxing Xu,Ke Zou,Yiming Qian,Rick Siow Mong Goh,Yong Liu,Huazhu Fu* 题目: A Data Augmentation Method and the Embedding Mechanism for Detection and...
CNN takes an average of 30 min for training, while the CDELM method takes only an average of 2.5 min. Based on the value of accuracy and duration of training time, the CDELM method had better performance than the conventional CNN method. Keywords: diabetic retinopathy; CNN architecture; ...
Automatic exudate extraction for early detection of Diabetic Retinopathy. In Proceedings of the 2013 International Conference on Information Technology and Electrical Engineering (ICITEE), Yogyakarta, Indonesia, 7–8 October 2013; pp. 31–35. 23. Choi, W.J.; Choi, T.S. Automated pulmonary nodule...
On the contrary, diabetic macular edema (DME) is a type of disease that affects patients with diabetes and is related to the thickening of muscle which can be considered a complication of diabetic retinopathy. A study showed that 7.5 million people aged 40 years or older suffer from DME [4...
Risk of Blindness Among Patients with Diabetes and Newly Diagnosed Diabetic Retinopathy. Diabetes Care 2021, 44, 748–756. [CrossRef] 8. Sloan, G.; Selvarajah, D.; Tesfaye, S. Pathogenesis, diagnosis and clinical management of diabetic sensorimotor peripheral neuropathy. Nat. Rev. Endocrinol. ...
The com- plications associated with diabetes, such as neuropathy, nephropathy, retinopathy, and car- diovascular disease, which arise in both type 1 and type 2 diabetes, are core factors of severe morbidity, mortality, and huge economic burdens [1–8]. Therefore, screening at an early stage...
Gray wolf optimization-extreme learning machine approach for diabetic retinopathy detection. Front. Public Health 2022, 10, 925901. [CrossRef] 24. Atlam, M.; Torkey, H.; El-Fishawy, N.; Salem, H. Coronavirus disease 2019 (COVID-19): Survival analysis using deep learning and Cox ...
Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus PhotographsAccuracy of a Deep Learning Algorithm for Detection of Diabetic RetinopathyAccuracy of a Deep Learning Algorithm for Detection of Diabetic Retinopathy. JAMA 2016, 316, 2402–2410. ...
Hyperparameter tuning deep learning for diabetic retinopathy fundus image classification. IEEE Access 2020, 8, 118164–118173. [CrossRef] 36. Pawara, P.; Okafor, E.; Schomaker, L.; Wiering, M. Data augmentation for plant classification. In Proceedings of the International Conference on Advanced...