# Bone Anomaly Detection by Extracting Regions of Interest and Convolutional Neural Networks
## 5 Conclusions and Future Scope  
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. In the pre-processing section, a segmentation is performed on the images, and 4 different regions of the images including all the ROIs of the TW method are separated from the images. During the model training, the images are artificially augmented by resizing and rotating to increase the number of images and achieve higher accuracy. 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  
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