Mask Classification and Head Temperature Detection Combined with Deep Learning Networks

Farady, Isack; Lin, Chih-Yang; Rojanasarit, Amornthep; Prompol, Kanatip; Akhyar, Fityanul

doi:10.1109/bcwsp50066.2020.9249454

Cited by 16 publications

(6 citation statements)

References 14 publications

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“…Table 6 reflects for each task and team the average time needed to evaluate one case, the GPU memory consumption, the docker details for memory requirements (CPU/GPU) and the methods' Mask-RetinaNet (Farady et al, 2020), ResNet (He et al, 2016). Beyond the well-known Dice (Milletari et al, 2016) and binary cross-entropy losses, others such as focal loss (Lin et al, 2017) and blob loss (Kofler et al, 2022) were mentioned.…”

Section: Challenge Submission and Participating Teamsmentioning

confidence: 99%

“…Among all the submissions, only one team (TheGPU) proposed an alternative to a deep learning solution. The majority of the proposed methods were trained as pure segmentation solutions and a few teams submitted a detection+segmentation solution based on Mask-RCNN (He et al, 2017) or Mask Retina net (Farady et al, 2020). Across all tasks, when a deep learning solution was proposed, the UNet architecture was the most common choice.…”

Section: Challenge Submission and Participating Teamsmentioning

confidence: 99%

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Where is VALDO? VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021

Sudre¹,

Wijnen²,

Dubost³

et al. 2022

Preprint

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Imaging markers of cerebral small vessel disease provide valuable information on brain health, but their manual assessment is time-consuming and hampered by substantial intra-and interrater variability. Automated rating may benefit biomedical research, as well as clinical assessment, but diagnostic reliability of existing algorithms is unknown. Here, we present the results of the VAscular Lesions DetectiOn and Segmentation (Where is VALDO? ) challenge that was run as a satellite event at the international conference on Medical Image Computing and Computer Aided Intervention (MICCAI) 2021. This challenge aimed to promote the development of methods for automated detection and segmentation of small and sparse imaging markers of cerebral small vessel disease, namely enlarged perivascular spaces (EPVS) (Task 1), cerebral microbleeds (Task 2) and lacunes of presumed vascular origin (Task 3) while leveraging weak and noisy labels. Overall, 12 teams participated in the challenge proposing solutions for one or more tasks (4 for Task 1 -EPVS, 9 for Task 2 -Microbleeds and 6 for Task 3 -Lacunes). Multi-cohort data was used in both training and evaluation. Results showed a large variability in performance both across teams and across tasks, with promising results notably for Task 1 -EPVS and Task 2 -Microbleeds and not practically useful results yet for Task 3 -Lacunes. It also highlighted the performance inconsistency across cases that may deter use at an individual level, while still proving useful at a population level.

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Section: Challenge Submission and Participating Teamsmentioning

confidence: 99%

Section: Challenge Submission and Participating Teamsmentioning

confidence: 99%

Where is VALDO? VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021

Sudre¹,

Wijnen²,

Dubost³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…The IR thermal sensors used are based on the AMG8833 chipset and the adopted computational platform is the Raspberry Pi. In [23], Farady et al present a head temperature assessment and mask classification method based in a real-time deep learning model. They use a deep learning object detection method to create a mask position and head temperature detector using a popular one-stage object detection.…”

Section: B Teletermography For Adjunctive Diagnostic Screeningmentioning

confidence: 99%

Contactless Smart Screening in Nursing Homes: an IoT-enabled solution for the COVID-19 era

Lopes

Pinho

Marques

et al. 2021

2021 17th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob)

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In the COVID-19 era, the provision of health indicators seamlessly and without contact, in groups at risk such as the elderly, is crucial due to the fast spread of the disease and the need to act quickly to contain its evolution. Continuous monitoring of vital signs, such as body temperature and cardio-respiratory rates, can be vital in early detection and prediction of COVID-19, which rapidly progresses and particularly affects the elderly population in nursing homes. Conventional clinical methods used for monitoring vital signs are contact-based, require contact sensors that need to be precisely attached by a trained health professional, are less convenient for repeatable measurements, and not practical for long-term monitoring. On the other hand, contactless vital signs monitoring using radar-based techniques, or IR-thermal imaging, do not require the attachment of physical electrodes and can be of great value in health screening of patients and help health professionals in early detection of the COVID-19 in the elderly population, in the specific context of nursing houses. This work describes the design and specification of a low-cost contactless health screening system for nursing homes, and includes the design of an IoT Edge device that can be placed above the beds where patients rest, allowing the continuous acquisition of health information and its processing without any type of contact and invasiveness.

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“…In [20], the authors proposed a network to detect and capture the temperature of a specific point inside a predicted region. They additionally used RGB data for the ResNet50based RetinaNet model [21] to classify data into 3-classes evaluating how the facial mask is worn: ''good,'' ''bad,'' and ''none.''…”

Section: Introductionmentioning

confidence: 99%

Mask Detection and Classification in Thermal Face Images

Kowalczyk¹,

Sobotka²,

Rumiński³

2023

IEEE Access

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Face masks are recommended to reduce the transmission of many viruses, especially SARS-CoV-2. Therefore, the automatic detection of whether there is a mask on the face, what type of mask is worn, and how it is worn is an important research topic. In this work, the use of thermal imaging was considered to analyze the possibility of detecting (localizing) a mask on the face, as well as to check whether it is possible to classify the type of mask on the face. The previously proposed dataset of thermal images was extended and annotated with the description of a type of mask and a location of a mask within a face. Different deep learning models were adapted. The best model for face mask detection turned out to be the Yolov5 model in the ''nano'' version, reaching mAP higher than 97% and precision of about 95%. High accuracy was also obtained for mask type classification. The best results were obtained for the convolutional neural network model built on an autoencoder initially trained in the thermal image reconstruction problem. The pretrained encoder was used to train a classifier which achieved an accuracy of 91%.INDEX TERMS Deep neural networks, epidemic prevention, health infrastructure, mask area detection, mask type classification, thermal imaging.

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Mask Classification and Head Temperature Detection Combined with Deep Learning Networks

Cited by 16 publications

References 14 publications

Where is VALDO? VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021

Where is VALDO? VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021

Contactless Smart Screening in Nursing Homes: an IoT-enabled solution for the COVID-19 era

Mask Detection and Classification in Thermal Face Images

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