Dual-Channel Capsule Generative Adversarial Network Optimized with Golden Eagle Optimization for Pediatric Bone Age Assessment from Hand X-Ray Image

Bone age assessment (BAA) is mainly utilized for detecting the growth of pediatrics because a large number of bone diseases occur at young age. Several algorithms related to BAAs were used for detecting the maturity of bones, but it does not provide sufficient accuracy, and also increased the error rate. To deal with these problems, the dual-channel capsule generative adversarial network optimized with Golden eagle optimization (GEO) is proposed in this paper for pediatric BAA from hand X-ray image (DCCGAN-GEO-BAA-HX-ray). Initially, the input hand X-ray imageries are collected from the dataset of Radiological Society of North America (RSNA) pediatric bone age (BA). Then, region of interest (RoI) of input hand X-ray imageries is segmented based on Tsallis entropy-based multilevel 3D Otsu thresholding (TE-3D-Otsu). Here, TE-3D-Otsu method segments the RoI region of wrist, thumb, middle finger, little finger, which enhance the classification accuracy. Moreover, the segmented RoI is given to DCCGAN that predicts the BAA. Generally, the DCCGAN does not reveal any adoption of optimization methods to scale the optimum parameters to ensure accurate classification. Therefore, GEO is used for optimizing the weight parameters of DCCGAN. The proposed DCCGAN-GEO-BAA-HX-ray method is executed in MATLAB and its performance is examined under performance metrics such as accuracy, precision, sensitivity, F-scores, specificity, concordance correlation coefficient (CCC) and computational time. Finally, the proposed DCCGAN-GEO-BAA-HX-ray approach attains 14.68%, 7.142%, 9.23% and 4.65% higher accuracy, 38.18%, 12.02%, 11.56% and 7.59% lower computational time is compared with existing FRCNN-AF-SFO-BAA-HX-ray, DCNN-W-CTO-BAA-HX-ray, CNN-MLP-BAA-HX-ray and CNN-BAA-HX-ray methods.