###### Abstract
The presence, pattern, and velocity of blood flow detected by color Doppler can help differentiate between malignant (cancerous) and benign (non-cancerous) lung lesions. Previous studies have demonstrated that if the vessels inside a lung mass are irregularly distributed, convoluted, and mostly peripheral distributed, we can suspect a malignant lesion (angiogenesis), distinguishing them from benign lesions which retain an organized vascular structure [5]. Color Doppler can also be useful in guiding needle biopsies of lung tumors. By identifying blood vessels around and within a tumor, physicians can plan the safest path for the biopsy needle, avoiding major blood vessels to reduce the risk of bleeding. These characteristics are important for diagnosis, but they are not considered by other radiological diagnostic methods (CT and MRI), being revealed by Doppler US and CEUS (contrast enhanced ultrasound) [6; 7; 8; 9; 10; 11]. At present, CT-guided percutaneous biopsy is one of the main investigative tools for a pathological diagnosis of chest lesions, but it exposes the patient to a considerable amount of radiation without being able to dynamically display the puncture process in real time, resulting in possible complications. Contrast-enhanced ultrasound (CEUS) can be valuable in the preoperative evaluation of the biopsy site and in increasing the accuracy of the biopsy because it can effectively identify both necrotic and viable tissues within the lesion, as well as provide valuable information for the accurate design and execution of the preoperative puncture biopsy [6; 7; 8; 9; 10; 11]. Recent advancements in artificial intelligence (AI) have led to the development of deep learning-based models for analyzing lung nodules using imaging modalities such as CT and MRI, in the case of Rayscape [12] and Optellum [13], but they are insufficiently available and present the disadvantage of irradiation for screening and follow-up. Recent studies have demonstrated the potential of automated image analysis techniques to enhance the diagnostic accuracy of color Doppler ultrasound [14; 15; 16; 17; 18]. Machine learning algorithms, particularly deep learning convolutional neural networks, have shown promising results in the classification of lung lesions based on CT images [19; 20; 21]. AI-assisted ultrasound analysis is an emerging field, with studies demonstrating its potential for improving lesion classification and reducing diagnostic variability. However, the application of similar techniques to color Doppler ultrasound data remains a less explored area [22; 23; 24; 25; 26]. Current research endeavors are concentrated on the integration of advanced computational methods with ultrasonography in order to circumvent conventional constraints. For example, Doppler ultrasound can assist in the differentiation of benign and malignant tumors by assessing blood flow and vascular patterns in lung lesions. This is especially pertinent in the context of tumor angiogenesis, which is a critical factor in the growth and metastasis of cancer. The aim of this study is to develop a novel method for the efficient classification of lung lesions using automatic vascularization evaluation on color Doppler ultrasound with machine learning techniques. This hybrid approach combines the advantages of color Doppler ultrasound imaging with the power of machine learning algorithms to provide a cost-effective, highly accurate, non-invasive, and reliable diagnosis of lung lesions.