COVID-19 Detection Using CT Image Based On YOLOv5 Network

Qu, Ruyi; Yang, Yi; Wang, Yuwei

doi:10.48550/arxiv.2201.09972

Cited by 1 publication

(1 citation statement)

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“…Lu KJ et al used NanoDet as a detector for identifying and locating flames in the field of vision for model selection, achieving high accuracy (23). Qu R et al found promising results using YOLOv5 to identify and pinpoint anomalies in COVID-19 chest radiographs (24). Yu G et al proposed that the PP-PicoDet realtime object identification model has achieved cutting-edge results (25).…”

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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Section: Introductionmentioning

confidence: 99%