Abstract
The purpose of this study was to assess the diagnostic efficacy of an artificial neural network (ANN) in identifying postmenopausal women with low bone mineral density (BMD) by quantifying trabecular bone microarchitectures. The study included 53 post-menopausal women, who were classified as normal (n=17) and osteoporotic (n=36) according to T-scores. BMD was measured on the femoral neck by dual-energy X-ray absorptiometry. Morphological features were extracted to find optimum input variables by quantifying microarchitectures of trabecular bone. Principal component analysis was used to reduce the dimen-sionalities and improve classification accuracy. For the classification, a two-layered feed forward ANNs was designed using the Levenberg-Marquardt train-ing algorithm. The experimental results indicated the superior performance of the proposed approach for discriminating osteoporotic cases from normal.
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Lee, J.H., Hwang, Y.N., Park, S.Y., Kim, S.M. (2014). Diagnosis of Osteoporosis by Quantification of Trabecular Microarchitectures from Hip Radiographs Using Artificial Neural Networks. In: Pan, L., Păun, G., Pérez-Jiménez, M.J., Song, T. (eds) Bio-Inspired Computing - Theories and Applications. Communications in Computer and Information Science, vol 472. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45049-9_40
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DOI: https://doi.org/10.1007/978-3-662-45049-9_40
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