关键词: 脊柱侧弯, 深度学习, 类不平衡, 背部区域提取, 选择性核

Abstract:

Concerning the problems of the difference in the number of samples between classes and the diversity of image background caused by environmental factors in the screening method of adolescent scoliosis based on back images and deep learning， a scoliosis screening method was designed， including steps such as back image data enhancement， back region extraction， and scoliosis diagnosis. Firstly， an improved diffusion generation model based on double residual U-Net structure and Convolutional Self-Attention Mechanism （CSAM） was proposed to generate high-quality pseudo-samples for the minority class back images， so as to balance the class distribution. Secondly， a back region extraction model with multi-loss constraint balance was designed to identify and extract back region from the back image， so as to eliminate the influence of image background difference on the diagnosis model. Thirdly， based on the selective kernel feature extraction and Spatial Pyramid Pooling （SPP） technologies， a classification model was constructed to realize the early screening and severity diagnosis of scoliosis through the back region. Finally， by integrating the above methods， the computer software and mobile software were developed to facilitate the actual back image acquisition and scoliosis screening business. Experimental results show that on the self-made scoliosis dataset， the proposed method achieves 98.64% and 73.06% accuracy in scoliosis early screening and severity diagnosis tasks， respectively， which is 2.52 and 6.48 percentage points higher than that of ResNet101. It can be seen that the proposed method can complete the diagnosis of scoliosis conveniently and quickly， and has certain application scenarios in large-scale rapid screening of scoliosis.

Key words: scoliosis, deep learning, class imbalance, back region extraction, selective kernel