# Study on Image Classification Algorithm Based on Multi-Scale Feature Fusion and Domain Adaptation
## 4 Experiments
### Experimental Results Analysis  
#### 4.3.1 Experimental Results  
Two experimental tasks were established based on the medicine datasets: J8 RT \(\rightarrow\) Montgomery and Montgomery \(\rightarrow\) J8 RT. The results are summarized in Table 1. Additionally, four experimental tasks were set up using the Office-Home dataset, with the results detailed in Table 2.  
In the transfer tasks from J8 RT to Montgomery and vice versa, the MMTADAN demonstrated an exceptional performance. Specifically, for the J8 RT \(\rightarrow\) Montgomery task, the MMTADAN achieved an accuracy of 92.57%, significantly surpassing the DANN (83.24%) and LPJT (79.34%). The MMTADAN also attained precision and recall rates of 89.32% and 85.76%, respectively, highlighting its superior capability in correctly identifying pulmonary nodules. The F1 score reached 0.83, reflecting a well-balanced performance in the classification task.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Task** & **Method** & **Accuracy** & **Precision** & **Recall** & **F1** \\ \hline J8 RT \(\rightarrow\) Montgomery & Transformer & 38.12\% & 45.36\% & 10.45\% & 0.16 \\  & DANN & 83.24\% & 85.67\% & 35.78\% & 0.49 \\  & LPJT & 79.34\% & 60.21\% & 30.65\% & 0.40 \\  & CDAN & 65.78\% & 80.45\% & 60.12\% & 0.69 \\  & MMTADAN & 92.57\% & 89.32\% & 85.76\% & 0.83 \\ Montgomery \(\rightarrow\) J8 RT & Transformer & 30.47\% & 55.36\% & 12.15\% & 0.19 \\  & DANN & 75.84\% & 82.37\% & 40.56\% & 0.55 \\  & LPJT & 78.46\% & 65.29\% & 35.12\% & 0.46 \\  & CDAN & 70.23\% & 78.41\% & 55.48\% & 0.65 \\  & MMTADAN & 91.37\% & 85.78\% & 88.12\% & 0.87 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental results of various methods for the medicine datasets.  
For the Montgomery \(\rightarrow\) JSRT task, the MMTADAN again exhibited outstanding results, with an accuracy of 91.37%, well above the other methods. Notably, in the precision (85.78%) and recall (88.12%), the MMTADAN showcased its strong adaptability in the target domain, achieving an F1 score of 0.87, which confirms its advantage in handling imbalanced datasets.  
In the various transfer tasks of the Office-Home dataset, the MMTADAN consistently maintained its lead:  
Ar \(\rightarrow\) Pr: the MMTADAN achieved an accuracy of 85.67%, outperforming all other methods. Its precision and recall were 88.14% and 80.47%, respectively, demonstrating adaptability in diverse image processing tasks;  
Pr \(\rightarrow\) Rw: the MMTADAN also displayed a robust performance, with an accuracy of 83.42%, significantly higher than those of the DANN and CDAN;  
Rw \(\rightarrow\) Cl: the MMTADAN reached an accuracy of 82.57%, excelling in all metrics, indicating its robustness in cross-domain transfer tasks;  
Cl \(\rightarrow\) Ar: with an accuracy of 86.24% and a precision of 88.42%, the MMTADAN reaffirmed its exceptional capability in complex image classification tasks.  
Overall, the MMTADAN consistently exhibited superior accuracy, precision, recall, and F1 scores across all the experiments. These results not only validate its effectiveness in medical image classification and domain adaptation tasks but also highlight its adaptability and flexibility when confronted with diverse datasets and tasks. In summary, the MMTADAN's outstanding performance across multiple transfer tasks, particularly in the accuracy and F1 scores, underscores its potential as an effective domain adaptation method. A comparative analysis with other methods distinctly illustrates the MMTADAN's exceptional performance in handling complex datasets, further supporting its practical application value.