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

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dc.contributor.author Carrillo Larco, Rodrigo Martín
dc.contributor.author Castillo-Cara, M.
dc.contributor.author Hernández Santa Cruz, J.F.
dc.date.accessioned 2022-10-12T18:25:59Z
dc.date.available 2022-10-12T18:25:59Z
dc.date.issued 2022
dc.identifier.uri https://hdl.handle.net/20.500.12866/12393
dc.description.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 understand the classification process. SETTING: Bus stops from Lima, Peru. We used five images per bus stop. PRIMARY AND SECONDARY OUTCOME MEASURES: Bus stop images were classified according to COVID-19 risk into two labels: moderate or extreme. RESULTS: NASNetLarge outperformed the other CNNs except in the recall metric for the moderate label and in the precision metric for the extreme label; the ResNet152V2 performed better in these two metrics (85% vs 76% and 63% vs 60%, respectively). The NASNetLarge was further tuned. The best recall (75%) and F1 score (65%) for the extreme label were reached with data augmentation techniques. Areas close to buildings or with people were often classified as extreme risk. CONCLUSIONS: This feasibility study showed that CNNs have the potential to classify street images according to levels of COVID-19 risk. In addition to applications in clinical medicine, CNNs and street images could advance the epidemiology of COVID-19 at the population level. en_US
dc.language.iso eng
dc.publisher BMJ Publishing Group
dc.relation.ispartofseries BMJ Open
dc.rights info:eu-repo/semantics/restrictedAccess
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
dc.subject Street images classification en_US
dc.subject COVID-19 risk en_US
dc.subject Lima en_US
dc.subject convolutional neural networks en_US
dc.title Street images classification according to COVID-19 risk in Lima, Peru: a convolutional neural networks feasibility analysis en_US
dc.type info:eu-repo/semantics/article
dc.identifier.doi https://doi.org/10.1136/bmjopen-2022-063411
dc.subject.ocde https://purl.org/pe-repo/ocde/ford#3.03.05
dc.relation.issn 2044-6055


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