BAEN-SKCNN: A novel framework for scoliosis early screening and severity diagnosis using unclothed back images

Abstract

Scoliosis is a common spinal disease and it’s early screening is essential for planning treatment and avoiding deterioration. The traditional screening methods for scoliosis have the disadvantages of unnecessary radiation exposure, the dependence on equipment, and the high demand on operators. Although the advent of deep learning techniques provides a new perspective for rapid and convenient screening of scoliosis, the existing related research faces challenges caused by issues such as image background diversity, image size inconsistency, and class imbalance. In order to solve the about problems, a method based on BAEN-SKCNN is proposed for early screening and severity diagnosis of scoliosis using back images. Specifically, BAEN is constructed to extract the back region to improve the diagnostic accuracy and model universality. Spatial pyramid pooling and selective kernel network are used to construct SKCNN for early screening and severity diagnosis of scoliosis. On a self-made scoliosis dataset, the proposed method achieves 98 % accuracy for early screening and 73 % accuracy for severity diagnosis, respectively. Combined with the APP software developed, the proposed method can easily and quickly complete the diagnosis of scoliosis without the limitation of venues, equipment and personnel. It has a certain application prospect in the large-scale screening of scoliosis, and has certain clinical significance for improving the diagnostic rate of scoliosis.