###### Abstract  
Lung cancer still represents one of the main causes of cancer-related mortality, highlighting the necessity for precise, effective, and minimally intrusive diagnostic methods. This research presents an innovative approach to classifying lung lesions using Doppler ultrasound imagery combined with a feed-forward neural network (FNN). This study integrates Doppler mode ultrasound vascularization features--blood vessel area, tortuosity index, and orientation--into an FNN to classify lung lesions as benign or malignant. A dataset of 565 Doppler ultrasound pictures was extended using augmentation techniques to enhance robustness, yielding a training dataset of 3390 images. The FNN architecture was trained utilizing the Levenberg-Marquardt algorithm, achieving a classification accuracy of 98%, demonstrating its potential as a diagnostic aid. The results indicate that integrating all three vascularization factors significantly improves diagnosis accuracy compared with individual modules. This method offers a non-invasive and cost-effective complementary tool to conventional techniques such as CT scans, with the potential to improve early detection and treatment planning for lung cancer patients. l 2025 15  
limited by radiation exposure and provide only indirect vascular characterization without the use of contrast agents. Although ultrasonography (US) has been extensively used in medicine for over 50 years, its role in lung assessment was initially limited due to the presence of air in the lungs, which hinders ultrasound penetration. However, in the past two decades, advancements in imaging techniques have demonstrated the utility of US in lung pathology assessment, particularly in detecting pleural and subpleural lesions. Though there were data concerning the acoustical properties of lung showing that high attenuation levels were present in the lung tissue sections at all levels of lung inflation, results from a more recent study underlines that the US attenuation properties of the lung are far from a precise cut-off [2]. So, for a long time, the lung was considered rather inapproachable by US, with only the pleura being commonly assessed. As more and more data have built up, we saw the definition of a lung US semiology and lung pattern recognition being defined and reported [3]. Currently, applications of ultrasonography have evolved from simple measurements of anatomical dimensions to the detection of small changes in tissue texture, evaluation of the vascularization with Doppler modes, and tissue perfusion with contrast agents. Also, ultrasonography offers several crucial advantages such as portability, no ionizing radiations exposure, low cost, short acquisition time, and sensitivity in determining the morphology of the organ, becoming one of the most significant, widely used, and versatile imaging techniques in medicine; it is a significantly valuable technique for the cancer's early diagnosis, being used to identify malignant and benign tissues in organs such as breast, thyroid, and many other organs [4]. The advantages of ultrasonography (US) were also proven during a previous study in which a comparative evaluation of lung lesions in contact with the thoracic wall by transthoracic US and computed tomography (CT) was performed. The study included 131 patients with thoraco-pulmonary peripheral lesions, demonstrated by CT [4]. As a result of evaluating US and CT, it was concluded that there is an agreement between the two imaging methods regarding tumor invasion, intratumoral necrosis, and peritumoral atelectasis in patients with thoracic lesions in contact with the thoracic wall [4]. There are also several studies on the use of Doppler mode US and CEUS for the needle biopsy of thoracic lesions and differential diagnosis, proving comparable results or even better results and less side effects in comparison with contrast-enhanced CT [5; 6; 7; 8; 9; 10]. Doppler US can assess blood flow in and around a lung tumor.