# CASVM: An Efficient Deep Learning Image Classification Method Combined with SVM
## 6 Conclusions  
The proposed image classification method based on the fusion of DL and SVM has three parts:  
1. We implement a combination of SVM and CNN, specifically replacing the softmax classifiers commonly used in CNNs with SVMs and training the network model using hinge loss function backpropagation to improve the generalization ability of image classification. As shown in Tables 4 and 6, CASVM has a higher accuracy and better classification performance in a general setting compared to softmax classification.
2. Based on this model, a CA mechanism is introduced to enhance network feature representation by aggregating features for two spatial directions to capture location information and channel relationships with low computational overhead. As shown in Table 7, the training time of CASVM is small compared with the benchmark model, and the classification accuracy is high. As shown in Tables 8 and 9, the model performance of CASVM is stable and robust compared to previous work.
3. The CASVM model is proposed to implement image classification, which uses the advantages of CNNs and SVMs and compensates for their disadvantages. CNNs using the backpropagation algorithm are prone to fall into local minima, and SVM can effectively avoid falling into local optima. However, if SVM alone is used for classification, it requires more complicated steps. CNNs can extract representative image features by convolving and downsampling the image. Therefore, the accuracy of image classification can be further improved by using CNNs to extract image features, and replacing the CNN classification layer with SVM, with excellent generalization ability.  
Figure 10: ROC curve of CASVM (ResNet18).  
Experiments showed that CASVM can effectively improve image classification accuracy. The higher F1-score of CASVM in different models not only improves the feature representation performance but also provides network models in different device environments to provide smaller computational overhead. With small overhead and good performance, CASVM provides a model reference for researchers and provides new options for research work on image classification, recognition, and segmentation with limited equipment. Although CASVM has high classification accuracy, many parameters require manual definition, the amount of training in this paper is fixed, and the numbers of deep network models and large-size datasets are insufficient. In addition, the interpretation of the specific position of CA in the network introduced in CASVM is not clear enough, and the appropriate position is derived by embedding CA in different positions in experiments. Our future work will focus on parameter adaption and a deeper network to optimize the algorithm, as well as improving the interpretation of CA position embedding to further improve classification accuracy.  
Conceptualization, J.P. and S.T.; methodology, J.P.; software, J.P.; validation, S.T., Y.L. and J.Z.; formal analysis, J.P.; investigation, J.P.; resources, S.T.; data curation, J.P.; writing--original draft preparation, J.P.; writing--review and editing, S.T.; visualization, J.P.; supervision, S.T.; funding acquisition, S.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Science and Technology Research Project (Youth) of Chongqing Municipal Education Commission (Project No. KJQN202101149).  
Not applicable.  
The data used to support the findings of this study are available from the corresponding author upon request.  
The authors declare no conflict of interest.