MSADCN:Multi-Scale Attentional Densely Connected Network for Automated Bone Age Assessment

Bone age assessment(BAA)helps doctors determine how a child’s bones grow and develop in clinical medicine.Traditional BAA methods rely on clinician expertise,leading to time-consuming predictions and inaccurate results.Most deep learning-based BAA methods feed the extracted critical points of images into the network by providing additional annotations.This operation is costly and subjective.To address these problems,we propose a multi-scale attentional densely connected network(MSADCN)in this paper.MSADCN constructs a multi-scale dense connectivity mechanism,which can avoid overfitting,obtain the local features effectively and prevent gradient vanishing even in limited training data.First,MSADCN designs multi-scale structures in the densely connected network to extract fine-grained features at different scales.Then,coordinate attention is embedded to focus on critical features and automatically locate the regions of interest(ROI)without additional annotation.In addition,to improve the model’s generalization,transfer learning is applied to train the proposed MSADCN on the public dataset IMDB-WIKI,and the obtained pre-trained weights are loaded onto the Radiological Society of North America(RSNA)dataset.Finally,label distribution learning(LDL)and expectation regression techniques are introduced into our model to exploit the correlation between hand bone images of different ages,which can obtain stable age estimates.Extensive experiments confirm that our model can converge more efficiently and obtain a mean absolute error(MAE)of 4.64 months,outperforming some state-of-the-art BAA methods.

Metaheuristic with Deep Learning Enabled Biomedical Bone Age Assessment and Classification Model

The skeletal bone age assessment(BAA)was extremely implemented in development prediction and auxiliary analysis of medicinal issues.X-ray images of hands were detected from the estimation of bone age,whereas the ossification centers of epiphysis and carpal bones are important regions.The typical skeletal BAA approaches remove these regions for predicting the bone age,however,few of them attain suitable efficacy or accuracy.Automatic BAA techniques with deep learning(DL)methods are reached the leading efficiency on manual and typical approaches.Therefore,this study introduces an intellectual skeletal bone age assessment and classification with the use of metaheuristic with deep learning(ISBAAC-MDL)model.The presented ISBAAC-MDL technique majorly focuses on the identification of bone age prediction and classification process.To attain this,the presented ISBAAC-MDL model derives a mask Region-related Convolutional Neural Network(Mask-RCNN)with MobileNet as baseline model to extract features.Followed by,the whale optimization algorithm(WOA)is implemented for hyperparameter tuning of the MobileNet method.At last,Deep Feed-Forward Module(DFFM)based age prediction and Radial Basis Function Neural Network(RBFNN)based stage classification approach is utilized.The experimental evaluation of the ISBAAC-MDL model is tested using benchmark dataset and the outcomes are assessed over distinct factors.The experimental outcomes reported the better performances of the ISBAACMDL model over recent approaches with maximum accuracy of 0.9920.

A Review on Dental Age Estimation in Children and Adolescents

Children are seen as beings who exist from birth through puberty,while teenagers are regarded as existing from puberty until around the age of 20 years.For a number of legal processes,including child labor,employment,the age of majority,rape,adoption,marriage eligibility,and situations where the birth certificate is unavailable,age estimation in children and adolescents is crucial.Despite the wide range of methods available,dental age estimation techniques that take into account tooth maturation are thought to be the most reliable predictors of chronological age in subadults.This is because genetic factors predominate and environmental factors,particularly between birth and age ten,tend to have little impact on tooth maturation.The eruption of teeth holds greater significance in the deciduous dentition,where genetic factors predominantly govern the process,compared to the permanent dentition.Conversely,tooth calcification serves as a viable indicator for estimating dental age in both primary and permanent dentitions.Current dental age estimation methods are based on age-related changes in teeth,such as tooth growth and development,changes that occur after teeth form,and biochemical changes.Therefore,in this review article,we will explore the several methodologies used for dental age assessment in children and adolescents.

ASSESSMENT OF AUTOPSIC SAMPLES OF CAROTID ATHEROSCLEROSIS IN THE AGED BY INTRAVASCULAR ULTRASOUND

To assess the value of intravascular ultrasound in detecting carotid atherosclerosis,we compared the ultrasound images of 48 carotid artery segments from autopsies with their histological findings.The results showed that by intravascular ultrasonography one could distinguish between elastic and