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For: Yamanakkanavar N, Choi JY, Lee B. MRI Segmentation and Classification of Human Brain Using Deep Learning for Diagnosis of Alzheimer's Disease: A Survey. Sensors (Basel) 2020;20:E3243. [PMID: 32517304 DOI: 10.3390/s20113243] [Cited by in Crossref: 17] [Cited by in F6Publishing: 6] [Article Influence: 8.5] [Reference Citation Analysis]
Number Citing Articles
1 Dayananda C, Choi JY, Lee B. Multi-Scale Squeeze U-SegNet with Multi Global Attention for Brain MRI Segmentation. Sensors (Basel) 2021;21:3363. [PMID: 34066042 DOI: 10.3390/s21103363] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
2 Qiao H, Chen L, Zhu F. Ranking convolutional neural network for Alzheimer's disease mini-mental state examination prediction at multiple time-points. Comput Methods Programs Biomed 2022;213:106503. [PMID: 34798407 DOI: 10.1016/j.cmpb.2021.106503] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
3 Kanakaraj P, Ramadass K, Bao S, Basford M, Jones LM, Lee HH, Xu K, Schilling KG, Carr JJ, Terry JG, Huo Y, Sandler KL, Netwon AT, Landman BA. Workflow Integration of Research AI Tools into a Hospital Radiology Rapid Prototyping Environment. J Digit Imaging 2022. [PMID: 35266088 DOI: 10.1007/s10278-022-00601-2] [Reference Citation Analysis]
4 Zhou SP, Fei SD, Han HH, Li JJ, Yang S, Zhao CY. A Prediction Model for Cognitive Impairment Risk in Colorectal Cancer after Chemotherapy Treatment. Biomed Res Int 2021;2021:6666453. [PMID: 33688501 DOI: 10.1155/2021/6666453] [Reference Citation Analysis]
5 Loddo A, Buttau S, Di Ruberto C. Deep learning based pipelines for Alzheimer's disease diagnosis: A comparative study and a novel deep-ensemble method. Comput Biol Med 2021;:105032. [PMID: 34838263 DOI: 10.1016/j.compbiomed.2021.105032] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 4.0] [Reference Citation Analysis]
6 Albishri AA, Shah SJH, Kang SS, Lee Y. AM-UNet: automated mini 3D end-to-end U-net based network for brain claustrum segmentation. Multimed Tools Appl 2022;:1-24. [PMID: 35035265 DOI: 10.1007/s11042-021-11568-7] [Reference Citation Analysis]
7 Helaly HA, Badawy M, Haikal AY. Deep Learning Approach for Early Detection of Alzheimer's Disease. Cognit Comput 2021;:1-17. [PMID: 34745371 DOI: 10.1007/s12559-021-09946-2] [Reference Citation Analysis]
8 Bi X, Liu W, Liu H, Shang Q. Artificial Intelligence-based MRI Images for Brain in Prediction of Alzheimer's Disease. J Healthc Eng 2021;2021:8198552. [PMID: 34712461 DOI: 10.1155/2021/8198552] [Reference Citation Analysis]
9 Silva RDCD, Jenkyn TR, Carranza VA. Enhanced Pre-Processing for Deep Learning in MRI Whole Brain Segmentation using Orthogonal Moments. Brain Multiphysics 2022. [DOI: 10.1016/j.brain.2022.100049] [Reference Citation Analysis]
10 Al-adhaileh MH. Diagnosis and classification of Alzheimer's disease by using a convolution neural network algorithm. Soft Comput. [DOI: 10.1007/s00500-022-06762-0] [Reference Citation Analysis]
11 Hazarika RA, Abraham A, Sur SN, Maji AK, Kandar D. Different techniques for Alzheimer’s disease classification using brain images: a study. Int J Multimed Info Retr 2021;10:199-218. [DOI: 10.1007/s13735-021-00210-9] [Reference Citation Analysis]
12 Li TR, Wu Y, Jiang JJ, Lin H, Han CL, Jiang JH, Han Y. Radiomics Analysis of Magnetic Resonance Imaging Facilitates the Identification of Preclinical Alzheimer's Disease: An Exploratory Study. Front Cell Dev Biol 2020;8:605734. [PMID: 33344457 DOI: 10.3389/fcell.2020.605734] [Cited by in Crossref: 7] [Cited by in F6Publishing: 5] [Article Influence: 3.5] [Reference Citation Analysis]
13 Wang L, Wang H, Huang Y, Yan B, Chang Z, Liu Z, Zhao M, Cui L, Song J, Li F. Trends in the application of deep learning networks in medical image analysis: Evolution between 2012 and 2020. Eur J Radiol 2022;146:110069. [PMID: 34847395 DOI: 10.1016/j.ejrad.2021.110069] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
14 Bouchaour N, Mazouzi S. Deep pattern-based tumor segmentation in brain MRIs. Neural Comput & Applic. [DOI: 10.1007/s00521-022-07422-y] [Reference Citation Analysis]
15 Alawad M, Aljouie A, Alamri S, Alghamdi M, Alabdulkader B, Alkanhal N, Almazroa A. Machine Learning and Deep Learning Techniques for Optic Disc and Cup Segmentation – A Review. OPTH 2022;Volume 16:747-64. [DOI: 10.2147/opth.s348479] [Reference Citation Analysis]
16 Srinivasu PN, SivaSai JG, Ijaz MF, Bhoi AK, Kim W, Kang JJ. Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM. Sensors (Basel) 2021;21:2852. [PMID: 33919583 DOI: 10.3390/s21082852] [Cited by in Crossref: 18] [Cited by in F6Publishing: 6] [Article Influence: 18.0] [Reference Citation Analysis]
17 Yang J, Wang S, Wu T. Maximum mutual information for feature extraction from graph-structured data: Application to Alzheimer’s disease classification. Appl Intell. [DOI: 10.1007/s10489-022-03528-x] [Reference Citation Analysis]
18 Yamanakkanavar N, Lee B. A novel M-SegNet with global attention CNN architecture for automatic segmentation of brain MRI. Comput Biol Med 2021;136:104761. [PMID: 34426168 DOI: 10.1016/j.compbiomed.2021.104761] [Reference Citation Analysis]
19 Hendrickx JO, De Moudt S, Calus E, Martinet W, Guns PDF, Roth L, De Deyn PP, Van Dam D, De Meyer GRY. Serum Corticosterone and Insulin Resistance as Early Biomarkers in the hAPP23 Overexpressing Mouse Model of Alzheimer's Disease. Int J Mol Sci 2021;22:6656. [PMID: 34206322 DOI: 10.3390/ijms22136656] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
20 Zulkifley MA, Abdani SR, Zulkifley NH. Automated Bone Age Assessment with Image Registration Using Hand X-ray Images. Applied Sciences 2020;10:7233. [DOI: 10.3390/app10207233] [Cited by in Crossref: 9] [Cited by in F6Publishing: 1] [Article Influence: 4.5] [Reference Citation Analysis]
21 Sah M, Direkoglu C. A survey of deep learning methods for multiple sclerosis identification using brain MRI images. Neural Comput & Applic. [DOI: 10.1007/s00521-022-07099-3] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]