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Retinal Blood Vessels Extraction of Challenging Images.

, , , , , and . AusDM, volume 996 of Communications in Computer and Information Science, page 347-359. Springer, (2018)

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An Active Contour Model Based on Region Based Fitting Terms Driven by p-Laplace Length Regularization., , and . IEEE Access, (2018)Neural network based denoised methods for retinal fundus images and MRI brain images., and . IJCNN, page 1151-1157. IEEE, (2016)Active Contours Using Additive Local and Global Intensity Fitting Models for Intensity Inhomogeneous Image Segmentation., , , , and . Comput. Math. Methods Medicine, (2016)Identification of noise in the fundus images., , , , and . ICCSCE, page 191-196. IEEE, (2013)Strided U-Net Model: Retinal Vessels Segmentation using Dice Loss., , , , , and . DICTA, page 1-8. IEEE, (2018)An Improved Infrared and Visible Image Fusion Using an Adaptive Contrast Enhancement Method and Deep Learning Network with Transfer Learning., , , , , and . Remote. Sens., 14 (4): 939 (2022)Impact of Image Enhancement Technique on CNN Model for Retinal Blood Vessels Segmentation., , , , , , , and . IEEE Access, (2019)Artificial intelligence (AI) for medical imaging to combat coronavirus disease (COVID-19): a detailed review with direction for future research., , , , , and . Artif. Intell. Rev., 55 (2): 1409-1439 (2022)Study of the Noise Level in the Colour Fundus Images., and . ICDS, volume 9208 of Lecture Notes in Computer Science, page 159-168. Springer, (2015)Mini V-Net: Depth Estimation from Single Indoor-Outdoor Images using Strided-CNN., , and . VISIGRAPP (5: VISAPP), page 205-214. SCITEPRESS, (2020)