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For: Khagi B, Kwon GR. Pixel-Label-Based Segmentation of Cross-Sectional Brain MRI Using Simplified SegNet Architecture-Based CNN. J Healthc Eng 2018;2018:3640705. [PMID: 30510671 DOI: 10.1155/2018/3640705] [Cited by in Crossref: 14] [Cited by in F6Publishing: 3] [Article Influence: 3.5] [Reference Citation Analysis]
Number Citing Articles
1 Fradi M, Zahzah E, Machhout M. Real-time application based CNN architecture for automatic USCT bone image segmentation. Biomedical Signal Processing and Control 2022;71:103123. [DOI: 10.1016/j.bspc.2021.103123] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 3.0] [Reference Citation Analysis]
2 Chen SY, Lin C, Li GJ, Hsu YC, Liu KH. Hybrid Deep Learning Models with Sparse Enhancement Technique for Detection of Newly Grown Tree Leaves. Sensors (Basel) 2021;21:2077. [PMID: 33809537 DOI: 10.3390/s21062077] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
3 Czajkowska J, Badura P, Korzekwa S, Płatkowska-Szczerek A. Automated segmentation of epidermis in high-frequency ultrasound of pathological skin using a cascade of DeepLab v3+ networks and fuzzy connectedness. Comput Med Imaging Graph 2021;95:102023. [PMID: 34883364 DOI: 10.1016/j.compmedimag.2021.102023] [Reference Citation Analysis]
4 Lin Y, Xu D, Wang N, Shi Z, Chen Q. Road Extraction from Very-High-Resolution Remote Sensing Images via a Nested SE-Deeplab Model. Remote Sensing 2020;12:2985. [DOI: 10.3390/rs12182985] [Cited by in Crossref: 20] [Cited by in F6Publishing: 3] [Article Influence: 10.0] [Reference Citation Analysis]
5 Czajkowska J, Badura P, Korzekwa S, Płatkowska-Szczerek A. Deep learning approach to skin layers segmentation in inflammatory dermatoses. Ultrasonics 2021;114:106412. [PMID: 33784575 DOI: 10.1016/j.ultras.2021.106412] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
6 Lee B, Yamanakkanavar N, Choi JY. Automatic segmentation of brain MRI using a novel patch-wise U-net deep architecture. PLoS One 2020;15:e0236493. [PMID: 32745102 DOI: 10.1371/journal.pone.0236493] [Cited by in Crossref: 9] [Cited by in F6Publishing: 3] [Article Influence: 4.5] [Reference Citation Analysis]
7 Al-Mohannadi A, Al-Maadeed S, Elharrouss O, Sadasivuni KK. Encoder-Decoder Architecture for Ultrasound IMC Segmentation and cIMT Measurement. Sensors (Basel) 2021;21:6839. [PMID: 34696054 DOI: 10.3390/s21206839] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
8 Marwa F, Zahzah EH, Bouallegue K, Machhout M. Deep learning based neural network application for automatic ultrasonic computed tomographic bone image segmentation. Multimed Tools Appl 2022;81:13537-62. [PMID: 35194385 DOI: 10.1007/s11042-022-12322-3] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
9 Agnes SA, Anitha J. Appraisal of Deep-Learning Techniques on Computer-Aided Lung Cancer Diagnosis with Computed Tomography Screening. J Med Phys 2020;45:98-106. [PMID: 32831492 DOI: 10.4103/jmp.JMP_101_19] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
10 Adegun AA, Viriri S, Ogundokun RO, Versaci M. Deep Learning Approach for Medical Image Analysis. Computational Intelligence and Neuroscience 2021;2021:1-9. [DOI: 10.1155/2021/6215281] [Cited by in Crossref: 7] [Cited by in F6Publishing: 4] [Article Influence: 7.0] [Reference Citation Analysis]