Symmetry-Based Fusion Algorithm for Bone Age Detection with YOLOv5 and ResNet34

• Published in: Symmetry (Basel) Vol. 15; no. 7; p. 1377
• Main Authors: Sheng, Wenshun, Shen, Jiahui, Huang, Qiming, Liu, Zhixuan, Lin, Jiayan, Zhu, Qi, Zhou, Lan
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.07.2023
• Subjects: Accuracy; Age; Algorithms; Automation; Bones; Classification; Clinical medicine; Comparative analysis; Datasets; Deep learning; Feature extraction; Hand (anatomy); Image enhancement; Joints (anatomy); Machine learning; Neural networks; Object recognition; Symmetry; Target detection; X-rays; China
• ISSN: 2073-8994; 2073-8994
• DOI: 10.3390/sym15071377

Bone age is the chronological age of human bones, which serves as a key indicator of the maturity of bone development and can more objectively reflect the extent of human growth and development. The prevalent viewpoint and research development direction now favor the employment of deep learning-based bone age detection algorithms to determine bone age. Although bone age detection accuracy has increased when compared to more established methods, more work needs to be conducted to raise it because bone age detection is primarily used in clinical medicine, forensic identification, and other critical and rigorous fields. Due to the symmetry of human hand bones, bone age detection can be performed on either the left hand or the right hand, and the results are the same. In other words, the bone age detection results of both hands are universal. In this regard, the left hand is chosen as the target of bone age detection in this paper. To accomplish this, the You Only Look Once-v5 (YOLOv5) and Residual Network-34 (ResNet34) integration techniques are combined in this paper to create an innovative bone age detection model (YARN), which is then combined with the RUS-CHN scoring method that applies to Chinese adolescent children to comprehensively assess bone age at multiple levels. In this study, the images in the hand bone dataset are first preprocessed with number enhancement, then YOLOv5 is used to train the hand bone dataset to identify and filter out the main 13 joints in the hand bone, and finally, ResNet34 is used to complete the classification of local joints and achieve the determination of the developmental level of the detected region, followed by the calculation of the bone age by combining with the RUS-CHN method. The bone age detection model based on YOLOv5 and ResNet34 can significantly improve the accuracy and efficiency of bone age detection, and the model has significant advantages in the deep feature extraction of key regions of hand bone joints, which can efficiently complete the task of bone age detection. This was discovered through experiments on the public dataset of Flying Paddle AI Studio.