Street images classification according to COVID-19 risk in Lima, Peru: a convolutional neural networks feasibility analysis.

ABSTRACT:

Objectives

During the COVID-19 pandemic, convolutional neural networks (CNNs) have been used in clinical medicine (eg, X-rays classification). Whether CNNs could inform the epidemiology of COVID-19 classifying street images according to COVID-19 risk is unknown, yet it could pinpoint high-risk places and relevant features of the built environment. In a feasibility study, we trained CNNs to classify the area surrounding bus stops (Lima, Peru) into moderate or extreme COVID-19 risk.

Design

CNN analysis based on images from bus stops and the surrounding area. We used transfer learning and updated the output layer of five CNNs: NASNetLarge, InceptionResNetV2, Xception, ResNet152V2 and ResNet101V2. We chose the best performing CNN, which was further tuned. We used GradCam to unde

SUBMITTER: Carrillo-Larco RM 

PROVIDER: S-EPMC9485648 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature