The intelligent estimating of spinal column abnormalities by using artificial neural networks and characteristics vector extracted from image processing of reflective markers

Abstract

Spinal column abnormities such as kyphosis and lordosis are the most common deformity that normally compare to the standard norms. To classify the subjects into the healthy and abnormal groups based on the angle values of the standard norms, the aim of this study was to use the artificial neural network method as a standard way for realizing the spinal column abnormalities. In this way, 40 male students (26 ± 2 years old, 72 ± 2.5 kg weight, and 169 ± 5.5 cm height) volunteered for this research. The lumbar lordosis and thoracic kyphosis angles were analyzed using an image processing of 13 reflective markers set on the spines process of the thoracic and lumbar spine. Therefore, after analyzing the position of these markers, a characteristic vector was extracted from the lateral side of every subject. The artificial neural network was trained by using the characteristic vector extracted from the labeled image of that person to diagnose abnormalities. The results indicate that the high efficiency of this method as the CCR (train) and CCR (test) was about 96 and 93%, respectively. These results show that the neural network can be considered as a standard way to diagnose the spinal abnormalities. Moreover, the most important benefit of this method is the estimation of spinal column abnormalities without considering intermediate quantities, and also the standard norms of these intermediate quantities can be considered as a non-invasive method.

Keywords: Abnormality, spinal column, kyphosis, lordosis, neural network, classification

African Journal of Biotechnology Vol. 12(4), pp. 419-426