# Lenke Classification of Scoliosis Based on Segmentation Network and Adaptive Shape Descriptor
## 3 Experiments and Analysis
### Comparison of the Representative and Latest Methods  
For a complete evaluation, we selected two types of methods for comparison. The first type is the four representative shape descriptors involved in our classification framework: (1) shape context [41; 42], which is a classic shape descriptor and has important applications in many fields; (2) TAR [43], which has superior performance in overall and local shape description; (3) CBoW [44], which provides a new strategy to describe shapes with bag of visual words; and (4) Fourier descriptor [45], which has recently been used for shape representation in cerebral microbleed detection. The second type is the recently popular deep learning classification method that we performed on spinal X-ray images for Lenke classification, such as remarkable Resnet101 [46], the latest vision transformer [18], and Swim transformer [47]. All these compared methods achieved state-of-the-art for shape representation or image classification. All the experimental results are presented in Table 6, where the values marked in bold indicate the best performance. To further provide a visual comparison of the shape representation, we also conducted a content-based image retrieval experiment for the five compared shape descriptors. We input an original spine X-ray image as the query and retrieved the seven most similar results in the dataset using the proposed shape matching framework based segmentation network. The two groups of retrieval results are presented in Figures 16 and 17, respectively. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Compared Methods** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline Shape context & 0.428 & 0.320 & 0.381 & 0.348 \\ TAR & 0.320 & 0.309 & 0.345 & 0.326 \\ CBoW & 0.263 & 0.299 & 0.248 & 0.271 \\ Fourier descriptor & 0.571 & 0.595 & 0.548 & 0.556 \\ Swin transformer & 0.517 & 0.232 & 0.205 & 0.180 \\ Vision transformer & 0.708 & 0.505 & 0.534 & 0.503 \\ Resnet101 & 0.733 & 0.596 & 0.585 & 0.588 \\ OURS & **0.786** & **0.804** & **0.756** & **0.779** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Lenke classification results for the representative and latest methods. Bold means the best. Figure 16: _Cont_. Figure 16: Image retrieval example for different shape descriptors with the query of Lenke 4. Figure 17: _Cont._  
From the above result, we can draw the following conclusions. First, the proposed Lenke classification framework embedded with our adaptive shape descriptor achieves best results in all the evaluation indicators, which shows its  
Figure 17: Image retrieval example for different shape descriptors with the query of Lenke 0.  
outstanding performance compared with the other representative and latest methods. Specifically, compared with the shape context descriptor, we achieved an improvement of 35.8% in accuracy, 48.4% in precision, 37.5% in recall, and 43.1% in F1-score; compared with the Fourier descriptor, we achieved an improvement of 21.5% in accuracy, 20.9% in precision, 20.8% in recall, and 22.3% in F1-score. We also surpassed the other shape descriptors including CBoW and TAR for all metrics, and the improvement was markedly significant. This is mainly because our shape descriptor is especially designed for describing the microscopic shape distribution of the vertebrae, while the existing shape descriptors were mainly developed for objects with larger shape changes. Second, we achieved superior results compared with deep learning classification methods. Specifically, compared with Resnet101, we achieved an improvement of 5.3% in accuracy, 20.8% in precision, 17.1% in recall, and 19.1% in F1-score. Compared to the recent transformer methods that demonstrated conspicuous performance in image classification, such as Swin transformer and vision transformer, we achieved more significant performance improvements. This demonstrates that the Lenke classification of scoliosis in X-rays is indeed challenging.