A Cascade Model with Prior Knowledge for Bone Age Assessment

Li, Nanxin; Cheng, Bo-Chao; Zhang, Junran

doi:10.3390/app12157371

Cited by 3 publications

(3 citation statements)

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“…When detecting fashion image categories, there are numerous categories that have similar patterns, such as dress and skirt categories. Researchers are proposed to differentiate object-to-object detection in the previous research [42][43][44][45][46]. To extract suitable and efficient adjacent objects in fashion detection, ref.…”

Section: Research On Apparel Detection and Classificationmentioning

confidence: 99%

“…[66] proposed the YOLO method with a larger effective receptive field (ERF-YOLO), which would reduce the number of model parameters while maintaining the same performance. BAFPN [42] is a novel bidirectional Feature Pyramid Network that constructs precise object recognition networks using YOLOv4 and Adaptively Spatial Feature Fusion. Utilizing the Exponential Moving Average improves network performance.…”

Section: Development Of Yolo Versionsmentioning

confidence: 99%

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A Semi-Supervised Learning Approach for Automatic Detection and Fashion Product Category Prediction with Small Training Dataset Using FC-YOLOv4

2022

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Over the past few decades, research on object detection has developed rapidly, one of which can be seen in the fashion industry. Fast and accurate detection of an E-commerce fashion product is crucial to choosing the appropriate category. Nowadays, both new and second-hand clothing is provided by E-commerce sites for purchase. Therefore, when categorizing fashion clothing, it is essential to categorize it precisely, regardless of the cluttered background. We present recently acquired tiny product images with various resolutions, sizes, and positions datasets from the Shopee E-commerce (Thailand) website. This paper also proposes the Fashion Category—You Only Look Once version 4 model called FC-YOLOv4 for detecting multiclass fashion products. We used the semi-supervised learning approach to reduce image labeling time, and the number of resulting images is then increased through image augmentation. This approach results in reasonable Average Precision (AP), Mean Average Precision (mAP), True or False Positive (TP/FP), Recall, Intersection over Union (IoU), and reliable object detection. According to experimental findings, our model increases the mAP by 0.07 percent and 40.2 percent increment compared to the original YOLOv4 and YOLOv3. Experimental findings from our FC-YOLOv4 model demonstrate that it can effectively provide accurate fashion category detection for properly captured and clutter images compared to the YOLOv4 and YOLOv3 models.

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Section: Research On Apparel Detection and Classificationmentioning

confidence: 99%

Section: Development Of Yolo Versionsmentioning

confidence: 99%

A Semi-Supervised Learning Approach for Automatic Detection and Fashion Product Category Prediction with Small Training Dataset Using FC-YOLOv4

2022

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“…Finally, the maturity scores of all bones are added to determine the total bone maturity scores, and the bone age is determined according to those total bone maturity scores. Li NX et al., 2022 proposed a bone age assessment system incorporating prior knowledge of RUS-CHN with a MAE of 4.44 months (0.37 years) ( 20 ).…”

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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