  Scoliosis is a sideways curvature of the spine that most often is diagnosed
among young teenagers. It dramatically affects the quality of life, which can
cause complications from heart and lung injuries in severe cases. The current
gold standard to detect and estimate scoliosis is to manually examine the
spinal anterior-posterior X-ray images. This process is time-consuming,
observer-dependent, and has high inter-rater variability. Consequently, there
has been increasing interest in automatic scoliosis estimation from spinal
X-ray images, and the development of deep learning has shown amazing
achievements in automatic spinal curvature estimation. The main target of this
thesis is to review the fundamental concepts of deep learning, analyze how deep
learning is applied to detect spinal curvature, explore the practical deep
learning-based models that have been employed. It aims to improve the accuracy
of scoliosis detection and implement the most successful one for automated Cobb
angle prediction. Keywords: Scoliosis Detection, Spinal Curvature Estimation,
Deep Learning. i
