TY - JOUR
T1 - Street images classification according to COVID-19 risk in Lima, Peru
T2 - a convolutional neural networks feasibility analysis
AU - Carrillo-Larco, Rodrigo M.
AU - Castillo-Cara, Manuel
AU - Hernández Santa Cruz, Jose Francisco
N1 - Publisher Copyright:
© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY. Published by BMJ.
PY - 2022/9/1
Y1 - 2022/9/1
N2 - 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.
AB - 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.
KW - COVID-19
KW - EPIDEMIOLOGY
KW - PUBLIC HEALTH
UR - http://www.scopus.com/inward/record.url?scp=85138168412&partnerID=8YFLogxK
U2 - 10.1136/bmjopen-2022-063411
DO - 10.1136/bmjopen-2022-063411
M3 - Artículo (Contribución a Revista)
C2 - 36123096
AN - SCOPUS:85138168412
SN - 2044-6055
VL - 12
SP - e063411
JO - BMJ open
JF - BMJ open
IS - 9
M1 - e063411
ER -