## 4 Discussion  
This study highlights the efficacy of combining Doppler ultrasound imaging with machine learning algorithms for classifying lung lesions, providing a dependable, non-invasive alternative to conventional approaches. The modular methodology employed to assess vascularization traits, such as blood vessel area, tortuosity, and direction, was important in attaining precise lesion classification. The blood vessel area assessment module is capable of evaluating the vascularization patterns inside the area of interest (ROI). The findings indicated that malignant tumors frequently display diminished or uneven vascularization patterns inside the area of interest (ROI). Although this metric alone does not produce high classification accuracy, it is an essential aspect in the multi-parameter system, as seen by the enhanced performance of the feed-forward neural network (FNN) when integrated with other vascularization factors. The system's capacity to measure these changes was confirmed with a performance accuracy of 52% when utilized as an independent parameter. The tortuosity index module was essential in detecting abnormal vascularization patterns. Malignant lesions were frequently linked to highly curved and irregular vascular pathways, indicative of tumor-induced angiogenesis. The module assessed these abnormalities by juxtaposing actual vessel trajectories with their linear approximations. Although this module has shown little efficacy as an independent diagnostic instrument, its impact on the collective analysis was substantial. The results corroborate the idea that heightened vascular tortuosity is a significant marker of malignancy. The blood vessel orientation module had a superior standalone performance of the three, attaining an accuracy of 82% as an independent diagnostic instrument. This outcome corresponds with clinical observations that malignant tumors display aligned and confined vascular orientations, leading to reduced mean square deviation (MSD) values. In contrast, benign lesions characterized by a more radial arrangement of arteries demonstrated elevated MSD values. Quantifying the distribution of vessel slopes inside the ROI offers a reliable criterion for differentiating between diseased and normal vascularization patterns. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Actual/Predicted** & **Benign (Predicted)** & **Malignant (Predicted)** & **Total Cases** \\ \hline Benign (Actual) & 313 (TN) & 9 (FP) & 322 \\ \hline Malignant (Actual) & 12 (FN) & 344 (TP) & 356 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Confusion matrix. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Training Method** & **Neural Network** & **Performance** \\ \hline Levenberg–Marquardt & Feed-forward network & 98\% \\ Scale Conjugate Gradient & Feed-forward network & 97\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Diagnosis module accuracy for all parameters and different training methods. The module's reliance on accurate skeletonization underscores the necessity for meticulous preprocessing to preserve its efficacy. The integration of all three vascularization factors resulted in a substantial enhancement of the FNN's performance, with an accuracy of 95%. This discovery highlights the significance of integrating complimentary attributes to improve diagnostic precision. Moreover, the use of multiple training methods, including the Levenberg-Marquardt algorithm, achieved a maximum performance of 98%, hence reinforcing the system's reliability. Comparatively, AI-driven CT-based models such as Rayscape and Optellum achieve sensitivities exceeding 90% but require high computational resources and expose patients to radiation risks. Our method, integrating Doppler ultrasound with machine learning, achieves 98% accuracy while offering a non-invasive, cost-effective, and radiation-free alternative. However, further validation through direct benchmarking with CT-based AI models is necessary to fully assess its clinical utility.