## 5 Conclusions  
This research presents an innovative approach for lung lesions diagnosis using the integration of Doppler ultrasound imaging and a feed-forward neural network (FNN). This technique utilizes three vascularization parameters--blood vessel area, tortuosity index, and orientation--to deliver a highly accurate, radiation-free, and cost-effective lung cancer diagnostic tool. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Method** & **Accuracy** & **Sensitivity** & **Specificity** & **AUC** & **Radiation** \\ \hline Doppler US + FNN & 98\% & 96.5\% & 97.2\% & 0.985 & NO \\ \hline CT-based AI & 92–95\% & 91–94\% & 90–96\% & 0.95 & YES \\ \hline MRI-Based AI & 94–97\% & 92–96\% & 91–98\% & 0.96 & NO \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance comparative analysis. The modular methodology facilitated an in-depth examination of certain vascularization metrics, with the orientation module achieving the highest independent performance at 82% accuracy. Although the blood vessel area and tortuosity index modules exhibited lower independent accuracies (52% and 53%, respectively), their contributions were essential in improving the performance of the integrated system. Upon integrating all three parameters, the FNN attained a classification accuracy of 95%, which was then enhanced to 98% using advanced training techniques such as the Levenberg-Marquardt algorithm. While AI-driven CT-based models demonstrate high accuracy, they require specialized imaging infrastructure and expose patients to radiation risks. In contrast, the proposed method achieves 98% classification accuracy while being accessible, portable, and suitable for resource-limited settings. The proposed system signifies a substantial advancement in the early detection and classification of lung abnormalities. However, the dependence on manual segmentation in preprocessing creates variability, underscoring the necessity for automated segmentation methods. Furthermore, augmenting the dataset to encompass varied patient demographics and investigating alternative imaging techniques such as contrast-enhanced ultrasound (CEUS) may boost the system's relevance and efficacy. While these findings demonstrate strong diagnostic potential, further improvements are necessary to enhance clinical applicability and scalability such as: (1) automated segmentation for improved reproducibility, efficiency, and consistency; (2) validation on larger and more diverse datasets to ensure better model generalization; and even (3) integration with portable ultrasound devices, making it a viable point-of-care diagnostic tool. Conceptualization, R.-I.C. ; Data curation, A.S.; Formal analysis, R.R.-B. and C.A. ; Funding acquisition, R.R.-B. and C.A. ; Investigation, R.-I.C. ; Methodology, R.R.-B., R.-I.C. and A.C.; Project administration, R.R.-B. ; Resources, R.R.-B. and R.-I.C. ; Software, A.S.; Validation, A.S., R.R.-B. and A.C.; Supervision, R.R.-B., Visualization, R.-I.C. and C.A. ; Writing--original draft, A.S. and R.R.-B. ; Writing--review and editing, R.R.-B. and A.C. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Ethical review and approval were waived for this study due to its retrospective design, which relied on pre-existing ultrasound images collected as part of a previously approved clinical study no 30/2016 issued by the Ethical Committee of the \"Iuliu Hatigeanu\" University of Medicine and Pharmacy Cluj-Napoca. Patient consent was waived due to the retrospective nature of the study, which utilized pre-existing, de-identified ultrasound images obtained from a previously approved clinical investigation. The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding authors.