# Lenke Classification of Scoliosis Based on Segmentation Network and Adaptive Shape Descriptor
## 3 Experiments and Analysis
### Computational Complexity Analysis  
In this section, we focus on computational complexity analysis for the proposed Lenke classification framework of scoliosis, which consists of two parts: segmentation using deep learning and classification using the shape descriptor and classifier.  
First, we report the parameter memory as the evaluation indicator for the segmentation networks in Table 8, in which the input and output image are resized to 256 * 256. To provide a more intuitive and comprehensive comparison, a schematic diagram of the computational complexity requirements vs. the classification performance is presented in Figure 18. From the above results, the MANet evidently has the smallest params. However, if we regard the classification performance as a comprehensive consideration, the Unet++ has a moderate param size and operation count. Therefore, based on a balance of the classification and time complexity, we recommend Unet++ as the preferred segmentation network.  
Next, we performed computational complexity analysis for different classification methods, which mainly included feature extraction, training, and testing. From the results shown in Table 9, the proposed adaptive shape descriptor had minimum time consumption in the feature extraction among all shape descriptors and a moderate testing time. It is worth noting that shape context, TAR, and our method directly extracted features from a single image. Thus, there is no training process. For CBoW, the visual dictionary needs to be trained from the dataset, and the training time is relatively long. For deep learning methods, the feature extraction is included in the training time. Considering the actual performance and time complexity, we concluded that the proposed method is the best choice in the Lenke classification of scoliosis.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Compared Method** &
\begin{tabular}{c} **Segmentation** \\ **Networks** \\ \end{tabular} & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline \multirow{6}{*}{Cobb angle + criteria} & FPN & 0.484 & 0.398 & 0.531 & 0.455 \\  & PSPNet & 0.428 & 0.376 & 0.691 & 0.487 \\  & MANet & 0.475 & 0.327 & 0.654 & 0.436 \\  & Unet & 0.486 & 0.388 & 0.559 & 0.458 \\  & Segmenter & 0.373 & 0.304 & 0.469 & 0.369 \\  & Unet++ & 0.489 & 0.352 & 0.685 & 0.465 \\ \hline \multirow{6}{*}{Shape descriptor + KNN} & FPN & 0.712 & 0.742 & 0.616 & 0.673 \\  & PSPNet & 0.675 & 0.758 & 0.517 & 0.615 \\ \cline{1-1}  & MANet & 0.682 & 0.706 & 0.534 & 0.608 \\ \cline{1-1}  & Unet & 0.697 & 0.715 & 0.616 & 0.662 \\ \cline{1-1}  & Segmeter & 0.731 & 0.731 & 0.630 & 0.677 \\ \cline{1-1}  & Unet++ & 0.786 & 0.804 & 0.756 & 0.779 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Results of the classification framework based on Cobb angle measurement and shape description.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Networks** & **FPN** & **PSPNet** & **MANet** & **Unet** & **Segmenter** & **Unet++** \\ \hline Params & 28.51 M & 49.01 M & 19.42 M & 29.05 M & 25.74 M & 34.96 M \\ \hline \hline \end{tabular}
\end{table}
Table 8: Computational complexities of segmentation networks.