Krithika, J.K. and Norashikin, Y. and Porkumaran, K. and Prabakar, S. (2022) Automated Boneage Analysis Using Machine Learning. Lecture Notes in Electrical Engineering, 758. pp. 241-252. ISSN 18761100
Full text not available from this repository.Abstract
Bone age assessment is done to analyse the skeletal maturity according to their chronological age. This is done by radiograph method considering the left hand or wrist. Bone age assessment is done by comparing the chronological age to assess the endocrine disorders and pediatric syndromes. Earlier the manual method was used, where the radiologists compare the radiograph image with the atlas and estimate the age of the bone. In this study, the analysis and classification of the x-ray image of the left hand is experimented to determine the bone age. Here the bone age analysis method involves the segmentation of the image, feature extraction and classification using support vector machine(SVM). The results obtained is future used to assess the skeletal abnormalities. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Item Type: | Article |
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Additional Information: | cited By 0; Conference of 1st International Conference on Artificial Intelligence for Smart Community, AISC 2020 ; Conference Date: 17 December 2020 Through 18 December 2020; Conference Code:286319 |
Uncontrolled Keywords: | Classification (of information); Extraction; Feature extraction; Image analysis; Image segmentation, Bone age; Bone age assessment; Chronological age; Features extraction; Hand X-ray image; Machine-learning; Region-of-interest; Regions of interest; Support vectors machine; X-ray image, Support vector machines |
Depositing User: | Mr Ahmad Suhairi UTP |
Date Deposited: | 19 Dec 2023 03:23 |
Last Modified: | 19 Dec 2023 03:23 |
URI: | https://khub.utp.edu.my/scholars/id/eprint/17406 |