Modified residual attention network for abnormalities segmentation and detection in WCE images-Reference-Cited by-同舟云学术

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Modified residual attention network for abnormalities segmentation and detection in WCE images

Published:2024-01-14 Issue:9-10 Volume:28 Page:6923-6936
ISSN:1432-7643
Container-title:Soft Computing
language:en
Short-container-title:Soft Comput

Author:

Charfi Said,Ansari Mohamed El,Koutti Lahcen,Ellahyani Ayoub,Eljaafari Ilyas

Funder

Ministère de l’Education Nationale, de la Formation professionnelle, de l’Enseignement Supérieur et de la Recherche Scientifique

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s00500-023-09576-w.pdf

Reference36 articles.

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3. Cai S, Tian Y, Lui H, Zeng H, Wu Y, Chen G (2020) Dense-unet: a novel multiphoton in vivo cellular image segmentation model based on a convolutional neural network. Quant Imaging Med Surg 10(6):1275

4. Charisis VS, Katsimerou C, Hadjileontiadis LJ, Liatsos CN, Sergiadis GD (2013) Computer-aided capsule endoscopy images evaluation based on color rotation and texture features: an educational tool to physicians. In: Proceedings of the 26th IEEE International Symposium On Computer-Based Medical Systems, pp 203–208

5. Chu Y, Huang F, Gao M, Zou D-W, Zhong J, Wu W, Wang Q, Shen X-N, Gong T-T, Li Y-Y et al (2023) Convolutional neural network-based segmentation network applied to image recognition of angiodysplasias lesion under capsule endoscopy. World J Gastroenterol 29(5):879

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