# Study on Image Classification Algorithm Based on Multi-Scale Feature Fusion and Domain Adaptation
## 4 Experiments
### Experimental Results Analysis
#### 4.3.3 Visualization  
To conduct a more detailed analysis of the method's accuracy, confusion matrices were computed. Figure 6 illustrates the experimental results of the MMTADAN and the comparative methods for the JJ8 RT dataset. The confusion matrices in Figure 6 indicate that the Transformer method performs the worst. While the DANN, LPJT, and CDAN have improved the accuracy of health \begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Method**} & **JSRT** & **Montgomery** & **Office–Home** \\  & **SSIM/SPA/EPA** & **SSIM/SPA/EPA** & **SSIM/SPA/EPA** \\ \hline GIF & 0.876/3191/51 & 0.832/2171/107 & 0.814/3560/231 \\ WLS & 0.830/3659/91 & 0.837/3659/499 & 0.892/1239/763 \\ GGIF & 0.894/2673/32 & 0.889/3894/175 & 0.990/1284/46 \\ SPGIF & 0.876/6531/139 & 0.735/7922/646 & 0.961/2867/109 \\ ILS & 0.892/4150/41 & 0.754/5154/423 & 0.841/4294/23 \\ RGIF & 0.840/2579/118 & 0.881/5271/718 & 0.847/4571/225 \\ ZF & 0.792/3550/67 & 0.716/4891/675 & 0.841/3441/19 \\ T-NET & 0.985/673/15 & 0.993/865/30 & 0.983/764/16 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparative performance metrics of classification methods across different datasets. Figure 6: Experimental results of confusion matrices of various methods for the medicine dataset. status recognition, their overall performances remain average. In contrast, the MMTADAN demonstrates effective classification. The Receiver Operating Characteristic (ROC) curve evaluates the classifier performance by plotting the True-Positive Rate (TPR) against the False-Positive Rate (FPR). The precision-recall (PR) curve assesses the model performance by graphing the precision against the recall. The TPR and FPR are calculated using true positives (TPs), false positives (FPs), true negatives (TNs), and false negatives (FNs) for the ROC curve, while the precision and recall are computed for the PR curve. Figure 7 presents the ROC curves for different models for the Montgomery dataset. In the ROC curves, the closer a curve is to the upper left corner, the better its performance. The ROC curve corresponding to the MMTADAN is the nearest to the upper left corner, confirming its superior effectiveness. To validate the classification performances of the methods under consideration, a series of Monte Carlo experiments were conducted. For each experiment, the samples were randomly divided into three groups: training, validation, and testing. Care was taken to ensure that the numbers of samples for each type were approximately equal across all three groups. The training segments were used to train individual classifiers, while the validation segments were employed to train an alpha ensemble using the scores of the trained classifiers. Finally, the test segments were utilized to determine the performance. Two performance metrics were computed: the Balanced Accuracy (BAC) and F1 score (F1), which were estimated as the mean accuracy for each class and the harmonic mean of the precision and recall, respectively. The final results represent the averages of 100 Monte Carlo experiments. The experimental results are presented in Table 5.  
As shown in Table 5, the Transformer performed poorly on the Office-Home dataset (65% BAC), indicating that this method struggles with more complex or diverse datasets. While the DANN showed a good performance on the JSRT and Montgomery, its performance dropped to 75% on the Office-Home dataset. This may suggest that the DANN is sensitive to certain types of data or that there were significant differences in the feature distribution between the training and Office-Home datasets. Similarly, the LPT's