# Automated Bone Age Assessment with Image Registration Using Hand X-ray Images
## 2 Related Work  
A recent systematic review conducted by [PERSON] et al. [24] shows that many of the automated bone age assessment methods still rely on conventional machine learning methods, where 22 out of the 26 methods have applied hand-crafted features extractor as an input to a regressor model. In [25], optimal fusion rules are analyzed in order to find the best combination weights for the 17 regions of interest, where the extracted features are then passed to a least-square classifier. Rather than using a single method to extract the features, the work presented in [26] combines three feature extractors, which are histogram of oriented gradients, local binary pattern, and scale-invariant feature transform, which is then fed to a support vector machine (SVM) classifier. Although the extracted features cover various unique traits of the X-ray images, the computational burden is too high. In [9], the combination of SVM and SVR are used in order to complement the training process, where cross-correlation function between the tested regions of interest is used to produce the similarity scores. On the other hand, [PERSON] et al. [27] utilize wavelet features coupled with an 11-class fuzzy classifier to predict the bone age. The method that is presented in [28] combines both Fuzzy and neural networks (NN) classifiers to better predict the bone age using a combination of features that were extracted from each region of interest based on TW protocol. Contrary to the work in [28], [PERSON] et al. [8] use a NN classifier to identify the best-handcrafted features for bone age assessment among these five keypoints detector; SIFT, SURF, BRIEF, BRISK, and FREAK. They found out that SIFT detector produces the lowest mean error when it is coupled with a NN classifier. Instead of using iterative training process in the NN classifier, the work in [29] opts to use an extreme learning machine to train a single hidden NN using Moore-Penrose generalized inverse matrices. The networks between the input and hidden nodes are randomly assigned and remain fixed, while only the network between the hidden nodes and output is trained while using a single step update. [PERSON] et al. [10] is one of the earliest works that uses convolutional neural networks (CNN) in predicting the bone age by introducing their BoNet architecture. They have experimented with various compact CNN architectures, where the best prediction is obtained using a set of five layers CNN of 96, 2048, 1024, 1024, and 1024 filters. Usually, a compact CNN network consists of three to five CNN layers at most [30], and it is usually first pre-trained in a different domain to reduce the possibility of overfitting [31]. BoNet does not apply any pre-processing step, which will face difficulty if the input images are not captured in a standard orientation and scale. Therefore, the work in [32] has applied U-Net architecture to segment the hand regions from the background. The VGG-16 network is then used to perform regression on the bone age assessment. They have assessed three variants of regions of interest, which are the whole X-ray image, carpal region, and phalanges regions, where the whole X-ray image mode returns the lowest mean absolute error. Similarly, the work in [33] has also applied a combination of U-Net segmentation and VGG-16 architecture for bone age prediction. However, the VGG-16 architecture is applied in the form of classifiers instead of regressors, where the network is trained for a large number of output classes (240 classes). [PERSON] et al. [34] then improves on the network training process, where they use paced transfer learning where the regressor network is tuned from the top layer first until the last bottom layer. The performance of several deep learning architectures that include AlexNet, GoogleNet, and VGG-16 are analyzed in [35] by using a normalized and resized input images. They have generated synthetic data to augment their training dataset using geometric transformations, photometric transformations, noise injections, and color jittering.