Scoliosis is the deviation or curvature of the spine in both the lumbar and thoracic spine in a ‘C’ or ‘S’ shape, causing health problems. Scoliosis is measured in degrees and is performed manually by orthopedic radiologists. This diagnosis consumes time and human effort to determine the severity of the spinal deformity based on the angle of deviation or curvature (Cobb Angle). This research aims to automatically calculate the spine curvature angle in X-ray images using a convolutions neural network based on Mask R-CNN architecture for semantic segmentation of the backbone. The Knowledge Discovery in Databases (KDD) development methodology was used to acquire and construct the image dataset, train the process, and validate the generated modelings in Python and the TensorFlow framework with the Keras library. The statistical results obtained by applying the t-Student statistical test show that there is no statistically significant difference (p_value > 0.05) between the degree of manual deviation obtained by 3 specialists (average) and the degree of automatic deviation obtained using the proposed model. Furthermore, Pearson’s correlation coefficient of \(\text {r}=0.99\) indicates a high linear association between these evaluations and a relatively low error MAE \(= 2.34\) (degrees). Thus, the model detects scoliosis with similar accuracy to the specialists in significantly less time (15–25 s vs. 2–5 min). Therefore, the model helps physicians understand the severity of scoliosis better and prescribe the most appropriate clinical treatment.