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

Abstract

Bone age assessment (BAA) is widely employed in therapeutic investigation of endocrinology issues in children. BAA is usually carried out by radiological investigation of the left hand. Since manual BAA is prone to observer variations, it is needed to design automated BAA approaches. Recently developed deep learning (DL) models have demonstrated fascinating outcomes in automatic BAA. This paper presents a novel hyperparameter optimization based deep learning model for automated bone age assessment and classification (HPTDL-BAAC). The proposed HPTDL-BAAC technique aims to predict the bone age and classify it into several stages using X-ray images. The proposed model involves data normalization for pre-processing. For feature extraction, a Regional Convolutional Neural Network (RCNN) based mask with SqueezeNet is employed as a baseline model. For boosting the performance of SqueezeNet method, swallow swarm optimization (SSO) algorithm is used for hyperparameter optimization. Finally, SoftMax classifier-based age prediction and weighted extreme learning machine (WELM) based stage classification models are applied to determine the proper bone age and class label. A wide range of simulations were performed on Digital Hand Atlas (DHA) Database and the outcomes are examined with respect to several measures. The experimental outcomes highlighted the supremacy of HPTDL-BAAC technique over the other existing techniques.