# Bone Anomaly Detection by Extracting Regions of Interest and Convolutional Neural Networks
###### Abstract  
The most suitable method for assessing bone age is to check the degree of maturation of the ossification centers in the radiograph images of the left wrist. So, a lot of effort has been made to help radiologists and provide reliable automated methods using these images. This study designs and tests Alexnet and GoogLeNet methods and a new architecture to assess bone age. All these methods are implemented fully automatically on the DHA dataset including 1400 wrist images of healthy children aged 0 to 18 years from Asian, Hispanic, Black, and Caucasian races. For this purpose, the images are first segmented, and 4 different regions of the images are then separated. Bone age in each region is assessed by a separate network whose architecture is new and obtained by trial and error. The final assessment of bone age is performed by an ensemble based on the Average algorithm between 4 CNN models. In the section on results and model evaluation, various tests are performed, including pre-trained network tests. The better performance of the designed system compared to other methods is confirmed by the results of all tests. The proposed method achieves an accuracy of 83.4% and an average error rate of 0.1%. bone anomaly detection; image segmentation; ensemble method; CNN +
Footnote †: journal: _Appl. Syst. Innov._**2023**, 6, 21. [[https://doi.org/10.3390/asi6010021](https://doi.org/10.3390/asi6010021)]([https://doi.org/10.3390/asi6010021](https://doi.org/10.3390/asi6010021))
are time-consuming and depend on the knowledge and experience of radiologists [8]. Therefore, automated BAA (Bone age assessment) methods are very necessary to reduce errors and help radiologists [9]. The DHA digital hand atlas database system [10] is a general and comprehensive database for the evaluation of automatic methods of bone age determination. Although many methods have been proposed for bone age assessment, most of them have been trained on private or breed-specific databases, and only a limited number of them have been trained and tested on this database. In the methods proposed in [11; 12; 13], automatic assessment of bone age has been performed using image processing and computer vision and techniques such as SVM, histogram [14], and fuzzy classification, and among the methods based on deep neural networks (DNNs), the methods proposed in [15; 16; 17] have been trained and tested on this database. According to the results, more effective and accurate methods should be implemented on this general and comprehensive dataset. Despite the existence of deep learning methods for estimating bone age, there are the following challenges associated with them: 1. Neither of the existing systems is truly based on the TW method. 2. None of the existing methods extracted all fingers of the hand and did not employ the combination of scores of different parts of the hand. 3. In each of these methods, a CNN constructing architecture is used, but one should note that the construction of a proper architecture for CNN depends on the knowledge level of the designer. Accordingly, in this study, a fully automatic system based on DNNs has been provided, which has improved the results obtained on this database. The proposed system includes preprocessing, feature extraction, and classification steps. In the TW3 method, the radius, ulna, and joints of the three fingers are analyzed as ROIs (Figure 1--numbered 1 to 13). It is relatively more accurate than the GP method because it evaluates and scores the maturation of each bone independently. So, in this study, the regions R1, R2, R3, and R4 specified in Figure 1 are first extracted using image processing techniques in the preprocessing section after removing the redundant regions and specifying the hand region. The maturation level of each region is then calculated using a CNN in the feature extraction and classification steps. The rest of the study is organized as follows: In the Section 2, the literature review is given. The Section 3 describes the proposed system.