Hyperparameter optimization based deep convolution neural network model for automated bone age assessment and classification

Palaniswamy, Thangam

doi:10.1016/j.displa.2022.102206

Cited by 10 publications

(2 citation statements)

References 22 publications

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“…As to comparison with the most recent works [36,37], our model considers the nonlinearity and continuity simultaneously of skeletal development by converting the bone age label into a multi-point distribution label. In addition, we extracted the RoIs input model using YOLO v5, allowing the model to catch more detailed features and reach better assessment, with a 1.6 months reduction in MAE for our model compared to the work of [36].…”

Section: Discussionmentioning

confidence: 99%

A Cascade Model with Prior Knowledge for Bone Age Assessment

Cheng²,

Zhang³

2022

Applied Sciences

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Bone age is commonly used to reflect growth and development trends in children, predict adult heights, and diagnose endocrine disorders. Nevertheless, the existing automated bone age assessment (BAA) models do not consider the nonlinearity and continuity of hand bone development simultaneously. In addition, most existing BAA models are based on datasets from European and American children and may not be applicable to the developmental characteristics of Chinese children. Thus, this work proposes a cascade model that fuses prior knowledge. Specifically, a novel bone age representation is defined, which incorporates nonlinear and continuous features of skeletal development and is implemented by a cascade model. Moreover, corresponding regions of interest (RoIs) based on RUS-CHN were extracted by YOLO v5 as prior knowledge inputs to the model. In addition, based on MobileNet v2, an improved feature extractor was proposed by introducing the Convolutional Block Attention Module and increasing the receptive field to improve the accuracy of the evaluation. The experimental results show that the mean absolute error (MAE) is 4.44 months and significant correlations with the reference bone age is (r = 0.994, p < 0.01); accuracy is 94.04% for ground truth within ±1 year. Overall, the model design adequately considers hand bone development features and has high accuracy and consistency, and it also has some applicability on public datasets, showing potential for practical and clinical applications.

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Section: Discussionmentioning

confidence: 99%

A Cascade Model with Prior Knowledge for Bone Age Assessment

Cheng²,

Zhang³

2022

Applied Sciences

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“…In the Mask R-CNN is an effectual DL framework that combines the semantic segmentation and object detection procedure. It mostly contains 2 levels of functions such as generating region proposals and categorizing every generated proposal [20]. An input X-ray image was primarily got into a convolutional networks termed a backbone networks and its influence obtain varies dependent upon the needed trade-off amongst the efficiency, trained speed, and restricted due to the computation power.…”

Section: Module I: Feature Extraction Processmentioning

confidence: 99%

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

Duhayyim¹,

Malibari²,

Obayya³

et al. 2022

Computers, Materials &Amp; Continua

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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 5474 CMC, 2022, vol.73, no.3 experimental outcomes reported the better performances of the ISBAAC-MDL model over recent approaches with maximum accuracy of 0.9920.

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A pediatric bone age assessment method for hand bone X-ray images based on dual-path network

Wang,

Jin,

et al. 2023

Neural Comput & Applic

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Hyperparameter optimization based deep convolution neural network model for automated bone age assessment and classification

Cited by 10 publications

References 22 publications

A Cascade Model with Prior Knowledge for Bone Age Assessment

A Cascade Model with Prior Knowledge for Bone Age Assessment

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

A pediatric bone age assessment method for hand bone X-ray images based on dual-path network

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