## 3 Results and Discussion
### Classification Comparison
At this stage, most of the cancer cells grow in a wall-attached manner, and the maximum diameter of the infiltration area is less than  
Figure 5: The confusion matrix and ROC curve for the classification of the four pathological subtypes. (**a**,**c**) FFDC; (**b**,**d**) OFDC. 0.5 cm. When the infiltration area invades the blood vessels, lymphatic vessels, or adjacent to the pleura, or when the tumor shedding cells spread to the adjacent alveolar cavity or small airway, the tumor enters the invasive stage. Therefore, there is a possibility of overlap between MIA and the two other pathologies, pre-invasive lesions, and IA pathology. As such, the classification performance of MIA was slightly worse than that of other pathological subtypes. To further test the effectiveness of FFDC, we considered another clinically important three-category classification subtask in distinguishing the IA, MIA, and pre-invasive lesions. This subtask is urgently needed in clinical practice. The lesions corresponding to pre-invasive often require conservative treatment, emphasizing long-term follow-up, while MIA and IA require elective or immediate surgical treatment due to their poorer prognosis when compared to pre-invasive lesions. The experimental steps are the same as the above classification of four pathological subtypes; the results are shown in Figure 6.  
We also compared our experiments with other OFDC methods. The results are listed in Table 5. Compared with the previous literature in which a classifier is trained with one-time CTI to determine invasiveness, the FFDC method is seen as more effective. The FFDC classification accuracy was about 15.9% higher than that in [37], 21.33% than that in [21], 15.1% than that in [38], and 10% than that of the OFDC method trained with a traditional classifier. In addition, we found that the FFDC method seldom made misclassifications in discriminating between pre-invasive lesions and MIA in the three-category subtype classification. Only 1/10 MIA was misclassified as pre-invasive lesions, and no pre-invasive lesions were misclassified as MIA. It was shown that FFDC can learn the implicit relationship between the three categories. However, due to the overlap between MIA and two other pathologies, FFDC would misclassify MIA as IA or pre-invasive lesions. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Year**} & \multicolumn{2}{c}{**Number of Classes**} & \multicolumn{2}{c}{**Method**} & \multicolumn{2}{c}{**Diagnostic**} \\  & **Pre-Invasive** & **MIA** & **IA** & & **Accuracy** & **AUC** \\ \hline
2018 [37] & 205 & 316 & 130 & OFDC + DenseSharp & 64.1\% & — \\
2021 [21] & 225 & 335 & 180 & OFDC + joint deep learning model & 58.67\% & 0.81 \\
2021 [38] & 302 & 349 & 258 & OFDC + 3D multi-task deep learning network & 64.9\% & 0.82 \\  & 52 & 24 & 110 & OFDC + traditional classifier & 70\% & 0.89 \\
2022 [ours] & 55 & 29 & 153 & FFDC + traditional classifier & 80\% & 0.88 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of studies using different methods to determine the invasiveness. Figure 6: Experiments of the GGN subtype three-category classification with FFDC. (**a**) Confusion matrix; (**b**) ROC curve.