COVID-19 Detection Using CT Image Based On YOLOv5 Network

Qu, Ruyi; Yang, Yi; Wang, Yuwei

doi:10.1109/iaecst54258.2021.9695714

Cited by 17 publications

(4 citation statements)

References 7 publications

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“…The results showed a potential benefit in estimating the severity of the disease and that there is a good relationship with the conventional CT score [29]. Ruyi Qu et al proved the better result achieved with the AI models, in which MAP is 0.5 of chosen YOLOv5s at 0.623, 0.157, and 0.101 higher than Faster RCNN and EfficientDet, respectively [11].…”

Section: Machine Learning Experimental Resultsmentioning

confidence: 94%

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Medical Images Based Covid-19 Detection Survey

Kadim,

Saad Al-Mukhtar,

Mahdi Altaei

2023

JQCM

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In December 2019, COVID-19 appeared for the first time in Wuhan (Hubei Province, China), after which it quickly spread over the entire earth. The World Health Organization quickly designated COVID-19 a pandemic because of the high number of deaths and rapid global spread of the disease. Because of this, many facets of society have been impacted, and those effects may last for years to come. Therefore, COVID-19 detection methods were the focus of numerous research projects in the past. This has led to the development of the COVID-19 AI Detector, a specialized area of artificial intelligence-based research. In this paper, we survey all significant current efforts that have taken advantage of machine learning to COVID-19 detection and prediction. We first surveyed all datasets used in relevant research, and then summarized them in a table containing the link to that data. Then, we mention all the methodologies employed to detect the presence of COVID-19 using such datasets. Later, the challenges and difficulties that facing the concerned researches are reported, while the results of all interesting related work that lay ahead in this field before concluding this paper were discussed fairly

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Section: Machine Learning Experimental Resultsmentioning

confidence: 94%

“…The higher MAP indicates good performance of the model. The descriptive characteristics of the lung images for both healthy and sick cases were extracted and entered into the classifier based on ML, and the results of the experiment were very successful [11].…”

Section: Machine Learning Methodsmentioning

confidence: 99%

Medical Images Based Covid-19 Detection Survey

Kadim,

Saad Al-Mukhtar,

Mahdi Altaei

2023

JQCM

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“…is demonstrated that YOLO v5 was more accurate in identifying blood cells and could detect them in real-time, making it more suitable for clinical applications than SSD. Qu et al [34] applied YOLO v5 and Faster RCNN to the CT image detection of COVID-19 and conducted a comparative experiment using the same dataset. e results showed that the mAP of YOLO v5 reached 0.623, while that of Faster RCNN was only 0.466.…”

Section: E Results Of Evaluation Metricmentioning

confidence: 99%

Traditional Chinese Medicine Recognition Based on Target Detection

Huangfu

et al. 2022

Evidence-Based Complementary and Alternative Medicine

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Traditional Chinese medicine (TCM) is widely used in China, but the large variety can easily lead to difficulties in visual identification. This study aims to evaluate the availability of target detection models to identify TCMs. We have collected images of 100 common TCMs in pharmacies, and use three current mainstream target detection models: Faster RCNN, SSD, and YOLO v5 to train the TCM dataset. By comparing the metrics of the three models, the results show that the YOLO v5 model has obvious advantages in the recognition of a variety of TCM, the mean average accuracy of the YOLO v5 is 94.33% and the FPS has reached 75, this model has a smaller number of parameters and solves the problem of detection and occlusion for small targets. Our experiments prove that the target detection technology has broad application prospects in the detection of TCM.

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“…Qu R et al. found promising results using YOLOv5 to identify and pinpoint anomalies in COVID-19 chest radiographs ( 24 ). Yu G et al.…”

Section: Introductionmentioning

confidence: 99%

A real-time automated bone age assessment system based on the RUS-CHN method

Yang

Dai²,

Qin³

et al. 2023

Front. Endocrinol.

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BackgroundBone age is the age of skeletal development and is a direct indicator of physical growth and development in children. Most bone age assessment (BAA) systems use direct regression with the entire hand bone map or first segmenting the region of interest (ROI) using the clinical a priori method and then deriving the bone age based on the characteristics of the ROI, which takes more time and requires more computation.Materials and methodsKey bone grades and locations were determined using three real-time target detection models and Key Bone Search (KBS) post-processing using the RUS-CHN approach, and then the age of the bones was predicted using a Lightgbm regression model. Intersection over Union (IOU) was used to evaluate the precision of the key bone locations, while the mean absolute error (MAE), the root mean square error (RMSE), and the root mean squared percentage error (RMSPE) were used to evaluate the discrepancy between predicted and true bone age. The model was finally transformed into an Open Neural Network Exchange (ONNX) model and tested for inference speed on the GPU (RTX 3060).ResultsThe three real-time models achieved good results with an average (IOU) of no less than 0.9 in all key bones. The most accurate outcomes for the inference results utilizing KBS were a MAE of 0.35 years, a RMSE of 0.46 years, and a RMSPE of 0.11. Using the GPU RTX3060 for inference, the critical bone level and position inference time was 26 ms. The bone age inference time was 2 ms.ConclusionsWe developed an automated end-to-end BAA system that is based on real-time target detection, obtaining key bone developmental grade and location in a single pass with the aid of KBS, and using Lightgbm to obtain bone age, capable of outputting results in real-time with good accuracy and stability, and able to be used without hand-shaped segmentation. The BAA system automatically implements the entire process of the RUS-CHN method and outputs information on the location and developmental grade of the 13 key bones of the RUS-CHN method along with the bone age to assist the physician in making judgments, making full use of clinical a priori knowledge.

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COVID-19 Detection Using CT Image Based On YOLOv5 Network

Cited by 17 publications

References 7 publications

Medical Images Based Covid-19 Detection Survey

Medical Images Based Covid-19 Detection Survey

Traditional Chinese Medicine Recognition Based on Target Detection

A real-time automated bone age assessment system based on the RUS-CHN method

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