Addis Ababa Institute of Technology

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Automatic Image Analysis for Bone Age Determination
(Addis Ababa University, 2022-12) Andualem Wube; Dawit Assefa (PhD); Asfaw Atinafu (PhD)
Bone age evaluation is commonly performed through radiological assessment of the skeletal development of the left hand, and then is compared with the chronological age. However, despite the time taking process, such image based automated processing remains incredibly challenging to implement. An accurate method is important to study the development of different wrist and pelvis structures which can predict age effectively. The proposed age determination scheme is composed of two major steps a watershed based segmentation scheme used to generate segmented images which are used as inputs to a deep learning algorithm that is able to determine the age of a given subject. The deep learning scheme utilized the InceptionV3 architecture for its implementation. The model was trained, validated, and tested on radiological hand images acquired from the RSNA database. The model resulted in a mean square error of 4.4 months when compared against the available ground truth information. Overall, the results showed that the proposed bone age determination scheme comes with great promises.