Bone age assessment based on deep learning architecture

<span lang="EN-US">The fast advancement of technology has prompted the creation automated systems in a variety sectors, including medicine. One application is an bone age evaluation from left-hand X-ray pictures, which assists radiologists and pediatricians making decisions about growth status youngsters. However, one most difficult aspects establishing system selecting best approach for producing effective dependable predictions, especially when working with large amounts data. As part this work, we investigate use convolutional neural networks (CNNs) model to classify bone. The work’s dataset based on radiological society North America (RSNA) dataset. To address issue, developed tested deep learning architecture autonomous assessment, design new convolution network (DCNN) model. assessment measures that work are accuracy, recall, precision, F-score. proposed achieves 97% test accuracy classification.</span>