Given the increasing demand for assessment of adolescent growth and development, prediction of adolescent hand-bone age has become important in the field of medical image analysis. Recent convolutional neural networks(CNNs), especially the EfficientNetV2 model, have significantly improved the accuracy of bone-age prediction. In this study, we trained a CNN model based on EfficientNetV2 to predict bone age based on 10,000 X-ray images of adolescents hand bones. The model extracted deep features from X-ray images, and after training, predicted bone age with remarkable accuracy. Moreover, when tested on real dataset, the model reduced the mean absolute error (MAE) of predicted bone age to 0.752 years. Our study confirms that deep learning methods aid in medical image analysis. Our bone-age prediction model is both objective and quantitative, and will find applications in clinical practice and when inferring the developmental cycle of minors.

Bone age assessment is a vital indicator for evaluating the growth and development of children and adolescents. Traditional manual evaluation methods are inefficient and highly subjective, failing to meet modern clinical demands. Recent advancements in deep learning, especially in image recognition and medical image analysis, have provided new opportunities for automated and precise bone age assessment. This paper presents a comprehensive study on deep learning-based bone age prediction, covering data preprocessing, model architecture, training strategies, and evaluation methods. The proposed method, leveraging data augmentation, U-Net segmentation, and the EfficientNet V2 architecture, achieves high accuracy and robustness in bone age prediction, demonstrating significant potential for clinical applications and providing valuable insights for future research.

Bone age assessment (BAA) is a critical research topic in pediatric radiology, with growing interest in developing automated BAA methods. This study proposes a bone age prediction model integrating cluster analysis and convolutional neural network (CNN) regression, further enhanced by a multi-scale attention mechanism to construct a "divide-and-focus" dual-driven deep learning framework. Targeting age-sensitive regional features in hand radiographs, we innovatively design an adaptive spatial attention module that achieves hierarchical anatomical feature enhancement through saliency detection of attention-guided regions of interest (ROI). The algorithm first uses multiconstrained clustering of K methods to generate age-specific subsets, followed by parallel execution on each subset: 1) attention-guided ROI segmentation and feature enhancement; 2) validation of the base CNN regression networks (including ResNet, DenseNet and EfficientNetV2); 3) set of cross-subset models with Bayesian-optimized weighting strategies for final prediction. By synergistically integrating the data distribution priors with attention-driven anatomical priors, the method delivers interpretable solutions when performing medical image regression tasks. The modular design ensures compatibility with mainstream CNN architectures. The method will aid pediatric growth monitoring and the diagnosis of endocrine disorders.

This paper introduces a UAV-based multispectral model, YOLO-SPAD, for rapid, non-destructive estimation of relative chlorophyll content of tea leaves. This is crucial for predicting growth conditions, implementing precise irrigation and fertilization, and increasing tea yields. The model leverages the spectral separation of the tea tree canopy and the YOLOv8 neural network architecture for image segmentation. Multispectral imagery of the standardized tea plantation was captured by a UAV equipped with a five-channel camera, while corresponding SPAD values were measured using a SPAD-502Plus device. Based on the YOLOv8 model, it is proposed to incorporate a Spectral Fusion module to better adapt to multispectral characteristics. Four deep learning models for image segmentation were further compared. The improved YOLOv8 model achieved a segmentation mIoU of 91.61% for the tea tree canopy, outperforming the YOLOv5 (90.65%) and DeepLabv3+ (86.14%), which were improved using the same method. Five different band combinations, 43 vegetation indexes, and 40 texture features were analyzed to construct mapping transformation pairs for reflectance in the segmented area. This was applied to the SPAD prediction head to implement YOLO-SPAD. The YOLO-SPAD model was used to predict tea canopy SPAD using UAV multispectral imagery with a coefficient of determination (R²) of 0.85 and a low error (RMSE=2.14, MAE=1.79), providing accurate and stable predictions. This model supports dynamic monitoring of tea tree growth via UAV remote sensing, aiding crop nutrition improvement and precision agriculture implementation.

This paper proposes an algorithm of building keypoint matches on multimodal images by combining a bootstrap process and global information. The correct ratio of keypoint matches built with descriptors is typically very low on multimodal images of large spectral difference. To identify correct matches, global information is utilized for evaluating keypoint matches and a bootstrap technique is employed to reduce the computational cost. A keypoint match determines a transformation T and a similarity metric between the reference and the transformed test image by T. The similarity metric encodes global information over entire images, and hence, a higher similarity indicates the match can bring more image content into alignment, implying it tends to be correct. Unfortunately, exhausting triplets/quadruples of matches for affine/projective transformation is computationally intractable, when the number of keypoints is large. To reduce the computational cost, a bootstrap technique is employed that starts from single matches for a translation and rotation model, and goes increasingly to quadruples of four matches for a projective model. The global information screens for "good" matches at each stage and the bootstrap strategy makes the screening process computationally feasible. Experimental results show that the proposed method can establish reliable keypoint matches on challenging multimodal images of strong multimodality.