# Bone Anomaly Detection by Extracting Regions of Interest and Convolutional Neural Networks
## 2 Literature Review  
Deep learning algorithms have recently caused great developments in digital and medical image processing and have performed well in diagnosing various diseases. Moreover,  
Figure 1: Numbers 1 to 13 indicate the ROI regions in the TW method (R1 to R4 regions extracted from DHA images).  
various papers have been published on the application of DNNs in bone age diagnosis [18]. proposed a deep learning-based approach where GoogLeNet and OxfordNet architectures and a 15-layer architecture called BoNet were tested on DHA database images, with the highest accuracy of 0.79% for BoNet. Ref. [8] tested GoogleNet, ResNet, and CaffeNet architectures to determine bone age on private databases. Despite its good performance, this system required manual marking on the image to find the hand region. In [19], an automatic method based on GoogleNet, AlexNet, and VGGNet networks was introduced. The system was not verified with the small number of images tested and the developmental health of the people to whom the image belonged. Ref. [8] proposed a model that considered the region with the largest area as the hand region and used MobileNetV3 and MLP for feature extraction and classification. The MAE model error rate was 6.2. The above methods have not done any special classification on the images and perform based on the GP method. So, some studies provide more accurate methods based on the TW method. In these studies, the ROI regions are first extracted from the images, based on review and scoring of which the final diagnosis is made. In the [16] method, ROI regions were extracted by R-CNN and the classification process was performed using the ST-Resnet network. Both methods included private databases. As a result, the results could not be compared and repeated. In [15], 4 ROI regions were first extracted in the preprocessing step. 13 ROI regions including joints were then extracted by 4 Faster-RCNN networks. Feature extraction and determination of the maturation level of each area were performed by a VGG-net network, and the final decision was determined based on voting between the networks. In [20], 14 ROI regions were first extracted from the original image, and the performance of VGG-19, GoogleNet, and AlexNet models was then evaluated for age estimation in these regions. The accuracy obtained was about 67%, which was lower than other methods. In [21], six ROI regions were detected using R-CNN, and the 3-layer growth state of the regions was investigated and determined by Inception-SVR. This method was limited to the investigation of 6 small regions. Both these methods used the DHA database. The results suggested that one could hope to provide more effective methods with higher accuracy for this dataset. The weakness that can be seen in most of the previous works is:  
1. Not extracting hand areas automatically or extracting limited areas
2. The use of single class models which actually reduces the comprehensiveness of the model
3. Failure to use approaches such as collective intelligence such as ensemble algorithms to detect bone age from different areas of the hand