An Ensemble Convolutional Neural Networks for Detection of Growth Anomalies in Children with X-ray Images

Bone age assessment is a method that is constantly used for investigating growth abnormalities, endocrine gland treatment, and pediatric syndromes. Since the advent of digital imaging, for several decades the bone age assessment has been performed by visually examining the ossification of the left hand, usually using the G&P reference method. However, the subjective nature of hand-craft methods, the large number of ossification centers in the hand, and the huge changes in ossification stages lead to some difficulties in the evaluation of the bone age. Therefore, many efforts were made to develop image processing methods. These methods automatically extract the main features of the bone formation stages to effectively and more accurately assess the bone age. In this paper, a new fully automatic method is proposed to reduce the errors of subjective methods and improve the automatic methods of age estimation. This model was applied to 1400 radiographs of healthy children from 0 to 18 years of age and gathered from 4 continents. This method starts with the extraction of all regions of the hand, the five fingers and the wrist, and independently calculates the age of each region through examination of the joints and growth regions associated with these regions by CNN networks; It ends with the final age assessment through an ensemble of CNNs. The results indicated that the proposed method has an average assessment accuracy of 81% and has a better performance in comparison to the commercial system that is currently in use.