Universidad Peruana Cayetano Heredia
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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