# CASVM: An Efficient Deep Learning Image Classification Method Combined with SVM
## 5 Discussions
Since the Animal10 dataset is unbalanced in terms of distribution, the confusion matrix to evaluate the model with the highest CASVM classification accuracy improvement is given in Figure 9, i.e., the performance results of the model based on [PERSON] et al. [43] on the dataset. The numbers 0-9 on the axes in the figure represent the ten categories in the dataset. The matrix's diagonal can show the model's recognition accuracy for each category, and both models have better classification results due to the high distribution of data in category 0 and category 8. However, from Figure 9a, it can be seen that the misclassification rate of images is significant. In the data of classes 2 and 5, where the number of samples is small, the accuracy of the original model shows a significant decrease, and CASVM can maintain the stability of accuracy. For the class 5 image data with the smallest number of samples, the misclassification rate of CASVM is up to 14%, while the original model reaches 38%. In addition, we removed the CA module from CASVM and retrained it according to the configuration in Table 6, and the misclassification rate reached up to 17%. It shows that even without introducing CA to emphasize the features, the misclassification rate is much lower than that of the previous work. It also proves again that CASVM can improve image classification accuracy more effectively. In Table 8, it can be found that the model proposed by [PERSON] [22] et al. obtained the highest accuracy rate, comparing it with ResNet18. Table 9 specifies the performance evaluation metrics between the models, including accuracy, recall, and F1 score (F1). As seen from the table, CASVM can achieve better performance than CNN-Softmax when image data is limited, because the hinge loss diverges more slowly than the cross-entropy loss, and because it relies only on support vectors when performing image classification. In addition, CASVM outperforms previous work in three metrics, particularly the F1 score and precision, achieving even 100% precision in each category. We attribute this to using the CA attention mechanism in conjunction with SVM, i.e., emphasizing low-frequency features helps the model to classify more effectively. Figure 8: Test accuracy of different models. Table 9 and Figure 9 show that each model has different performance levels for the class 0 Cane, class 7 Pecora, and class 5 Gatto images. From Table 9, for CASVM (ResNet18), came and pecora have the lowest precision. For this reason, misclassification is further observed using the receiver operating characteristic (ROC), as shown in Figure 10, and compared using Gatto. The area under the curve (AUC) represents the classification ability of the model, and the higher the value, the higher the accuracy. The AUC values of Cane, Pecora, and Gatto are 0.9745, 0.9769, and 0.9933, respectively. The AUC values of cane and pecora are significantly lower than those of gatto. We focused on the image data and excluded the loss of classification accuracy brought on by remarkably similar features. As a result, the unbalanced distribution of the dataset and the lack of samples from this class in the training dataset are to blame for Cane with Pecora's low classification accuracy. Figure 8 and Table 9 show that CASVM can still maintain some classification stability in the presence of subpar image data, despite this.