nasnetmobile (Kaggle Inc)
Structured Review

Nasnetmobile, supplied by Kaggle Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/nasnetmobile/nasnetmobile/pmc11520796-1-13-11
Average 90 stars, based on 1 article reviews
Images
1) Product Images from "Deep learning approach to predict autism spectrum disorder: a systematic review and meta-analysis"
Article Title: Deep learning approach to predict autism spectrum disorder: a systematic review and meta-analysis
Journal: BMC Psychiatry
doi: 10.1186/s12888-024-06116-0
Figure Legend Snippet: Characteristics of the included studies in the meta-analysis
Techniques Used: Extraction, Imaging
Figure Legend Snippet: Classification performance of deep learning for ASD in the included studies
Techniques Used:
Related Articles
Extraction:Article Title: Deep learning approach to predict autism spectrum disorder: a systematic review and meta-analysis Article Snippet: et al. [ ] , Bangladesh , 866 , ABIDE , AAL atlas, BASC atlas, CC200 Atlas, and Power atlas , Brain Imaging. .. Alsaade et al. [ ] , Saudi Arabia , 300 , Article Title: A multi-view convolutional neural network method combining attention mechanism for diagnosing autism spectrum disorder Article Snippet: et al. [ ], in 2021, improved the graph convolutional neural network to identify brain diseases with an accuracy of 67.3%. .. Alsaade et al. (2022) [ ] evaluated the performance of three deep learning models, VGG19, Imaging:Article Title: Deep learning approach to predict autism spectrum disorder: a systematic review and meta-analysis Article Snippet: et al. [ ] , Bangladesh , 866 , ABIDE , AAL atlas, BASC atlas, CC200 Atlas, and Power atlas , Brain Imaging. .. Alsaade et al. [ ] , Saudi Arabia , 300 , Article Title: A multi-view convolutional neural network method combining attention mechanism for diagnosing autism spectrum disorder Article Snippet: et al. [ ], in 2021, improved the graph convolutional neural network to identify brain diseases with an accuracy of 67.3%. .. Alsaade et al. (2022) [ ] evaluated the performance of three deep learning models, VGG19, |