The diagnosis of spinal deformities is one of the most frequent daily clinical routine. X-ray images are used to diagnose several pathologies in order to reduce harmful radiations of the patient. Spinal deformities are diagnosed essentially from vertebral shapes, orientations, and positions, so their detection and segmentation are major steps required for diagnosis. Deep learning could be applied for automatic diagnosis to detect scoliosis and its variants with a favourable performance. In this study, based on 609 spinal anterior-posterior x-ray images obtained from the public SpineWeb, we examine generative ad- versarial network (GAN) based architectures and convolutional neural network (CNN) based architectures models that are capable of automatically detecting anomalies in radiograph and achieve expert-level performances in various fields providing a solid comparative study. Most of the implemented models are apt to automatically distinguish limits between vertebrae so determining their shape with a very good visual performance. The GAN-based architecture estimates the required vertebral landmarks with an accuracy rate of 0.966, signify its capacity for automatic scoliosis assessment in a clinical setting.