Abstract: Bone age assessment and its comparison with the chronological age is a crucial task to determine the disorders and their effects on the bone when there are fewer documents. It is a time-consuming activity that is performed by the doctors by the method known as ossification. It can be automated with machine learning techniques. In the proposed system, the images of hand radiographs are preprocessed using data augmentation and the feature extraction is done using pre-trained Mobilenet and Xception models. The obtained results have shown that the Xception model gives the best MAE as compared with Mobilenet.

Keywords: Bone Age Assessment, X-ray images, Xception, Mobilenet, Transfer Learning, Deep Learning.

PDF | DOI: 10.17148/IJARCCE.2022.11450

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