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

Abstract

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 ISBAAC-MDL model over recent approaches with maximum accuracy of 0.9920.