Analyzing Associations Between Chronic Disease Prevalence and Neighborhood Quality Through Google Street View Images.
IEEE Access
; 8: 6407-6416, 2020.
Article
em En
| MEDLINE
| ID: mdl-33777591
ABSTRACT
Deep learning and, specifically, convoltional neural networks (CNN) represent a class of powerful models that facilitate the understanding of many problems in computer vision. When combined with a reasonable amount of data, CNNs can outperform traditional models for many tasks, including image classification. In this work, we utilize these powerful tools with imagery data collected through Google Street View images to perform virtual audits of neighborhood characteristics. We further investigate different architectures for chronic disease prevalence regression through networks that are applied to sets of images rather than single images. We show quantitative results and demonstrate that our proposed architectures outperform the traditional regression approaches.
Texto completo:
1
Base de dados:
MEDLINE
Tipo de estudo:
Prevalence_studies
/
Prognostic_studies
/
Risk_factors_studies
Idioma:
En
Revista:
IEEE Access
Ano de publicação:
2020
Tipo de documento:
Article