Visual Object Detection with DETR to Support Video-Diagnosis Using Conference Tools

Biró, Attila; Jánosi-Rancz, Katalin Tünde; Szilágyi, László; Cuesta‐Vargas, Antonio; Martín-Martín, Jaime; Szilagyi, S.M.

doi:10.3390/app12125977

Cited by 10 publications

(21 citation statements)

References 30 publications

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“…The first phase of this research validates the medical radar sensors’ usability in the sports sector; the real-time remote data acquisition process [ 24 ] and the initial feasibility validation experiments were conducted based on that. In the validation phase, the research participants were professional athletes, with a minimum of 30 activities per player sample of participants.…”

Section: Materials and Methodsmentioning

confidence: 99%

“…The data adjustments were conducted with dedicated Python scripts. For analysis, we used Google Colab Platform [ 24 ].…”

Section: Materials and Methodsmentioning

confidence: 99%

“…The modeling and AI experiments were run on the following configuration: MBO Gigabyte Z390 Aorus Pro, CPU INTEL Core i7-8700K 3.7 GHz 12 MB LGA1151, DDR4 32 GB 3600 MHz Kingston HyperX Predator Black CL17 KIT2, VGA MSI RTX 2080 Ventus 8 GB, SSD M.2 SAMSUNG 970 Pro 1 TB, Corsair RMx (2018) 750 W Modular 80+ Gold, as well as dedicated notebooks on the Google Colab platform [ 24 ].…”

Section: Materials and Methodsmentioning

confidence: 99%

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Machine Learning on Prediction of Relative Physical Activity Intensity Using Medical Radar Sensor and 3D Accelerometer

Biró

Szilagyi

Szilágyi

et al. 2023

Sensors

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Background: One of the most critical topics in sports safety today is the reduction in injury risks through controlled fatigue using non-invasive athlete monitoring. Due to the risk of injuries, it is prohibited to use accelerometer-based smart trackers, activity measurement bracelets, and smart watches for recording health parameters during performance sports activities. This study analyzes the synergy feasibility of medical radar sensors and tri-axial acceleration sensor data to predict physical activity key performance indexes in performance sports by using machine learning (ML). The novelty of this method is that it uses a 24 GHz Doppler radar sensor to detect vital signs such as the heartbeat and breathing without touching the person and to predict the intensity of physical activity, combined with the acceleration data from 3D accelerometers. Methods: This study is based on the data collected from professional athletes and freely available datasets created for research purposes. A combination of sensor data management was used: a medical radar sensor with no-contact remote sensing to measure the heart rate (HR) and 3D acceleration to measure the velocity of the activity. Various advanced ML methods and models were employed on the top of sensors to analyze the vital parameters and predict the health activity key performance indexes. three-axial acceleration, heart rate data, age, as well as activity level variances. Results: The ML models recognized the physical activity intensity and estimated the energy expenditure on a realistic level. Leave-one-out (LOO) cross-validation (CV), as well as out-of-sample testing (OST) methods, have been used to evaluate the level of accuracy in activity intensity prediction. The energy expenditure prediction with three-axial accelerometer sensors by using linear regression provided 97–99% accuracy on selected sports (cycling, running, and soccer). The ML-based RPE results using medical radar sensors on a time-series heart rate (HR) dataset varied between 90 and 96% accuracy. The expected level of accuracy was examined with different models. The average accuracy for all the models (RPE and METs) and setups was higher than 90%. Conclusions: The ML models that classify the rating of the perceived exertion and the metabolic equivalent of tasks perform consistently.

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Section: Materials and Methodsmentioning

confidence: 99%

“…The data adjustments were conducted with dedicated Python scripts. For analysis, we used Google Colab Platform [ 24 ].…”

Section: Materials and Methodsmentioning

confidence: 99%

Section: Materials and Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning on Prediction of Relative Physical Activity Intensity Using Medical Radar Sensor and 3D Accelerometer

Biró

Szilagyi

Szilágyi

et al. 2023

Sensors

Self Cite

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“…Thus, Investigating DETER in lung cancer classification seems quite interesting as it meets our need to localize and classify tumors in a single model. In addition, DTER is also a fast model that has proven since 2020 superior performance than CNN models in several fields 44 …”

Section: Related Workmentioning

confidence: 99%

“…In addition, DTER is also a fast model that has proven since 2020 superior performance than CNN models in several fields. 44 3 | AUTOMATIC LUNG CANCER DETECTOR WITH TRANSFORMERS…”

Section: Related Workmentioning

confidence: 99%

A transformer‐based deep neural network for detection and classification of lung cancer via PET/CT images

Barbouchi

Hamdi

Elouedi

et al. 2023

Int J Imaging Syst Tech

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Lung cancer is the leading cause of death for men and women worldwide and the second most frequent cancer. Therefore, early detection of the disease increases the cure rate. This paper presents a new approach to evaluate the ability of positron emission tomography/computed tomography (PET/CT) images to classify and detect lung cancer using deep learning techniques. Our approach aims to fully automate lung cancer's anatomical localization from PET/CT images. It also searches to classify the tumor, which is essential as it makes it possible to determine the disease's speed of progression and the best treatments to adopt. We have built, in this work, an approach based on transformers by implementing the DETR model as a tool to detect the tumor and assist physicians in staging patients with lung cancer. The TNM staging system and histologic subtype classification were both taken as a standard for classification. Experimental results demonstrated that our approach achieves sound results on tumor localization, T staging, and histology classification. Our proposed approach detects tumors with an intersection over union (IOU) of 0.8 when tested on the Lung-PET-CT-Dx dataset. It also has yielded better accuracy than state-of-the-art T-staging and histologic classification methods. It classified T-stage and histologic subtypes with an accuracy of 0.97 and 0.94, respectively.

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Galaxy detection and classification in sky images with neural network

Szabó,

Pintér

2023

2023 IEEE 23rd International Symposium on Computational Intelligence and Informatics (CINTI)

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Visual Object Detection with DETR to Support Video-Diagnosis Using Conference Tools

Cited by 10 publications

References 30 publications

Machine Learning on Prediction of Relative Physical Activity Intensity Using Medical Radar Sensor and 3D Accelerometer

Machine Learning on Prediction of Relative Physical Activity Intensity Using Medical Radar Sensor and 3D Accelerometer

A transformer‐based deep neural network for detection and classification of lung cancer via PET/CT images

Galaxy detection and classification in sky images with neural network

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