Accurate and automatic localization of the common carotid artery (CCA) is extremely important because the narrowing of the CCA is a silent disease. CCA disease doesn't cause any symptoms in its early stages, and people don't realize that they usually have a problem until they have a stroke. A stroke occurs when the brain doesn't receive enough blood for a long time. Brain damage from a stroke can lead to loss of speech or vision, and major strokes can cause death. In this paper, we proposed

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... ous techniques to localize the CCA in transverse section ultrasound (US) images using deep learning. First, we applied preprocessing to the images in the dataset before detecting the bounding box containing the CCA. We used a faster regional proposal convolutional neural network (Faster R-CNN) to detect the rectangular region (bounding box) around the CCA. Then we applied various localization techniques to localize the CCA in the US images. The proposed method has been performed on ultrasonic transverse images of the signal processing (SP) Lab. We compared our results with the clinicians' circles obtaining a great match between them. The accuracy of the bounding box detection was 97.5 and a Jaccard similarity of 90.86% between our proposed system and the clinicians' manual circles. Our proposed system has shown results that outperform other systems in Literature.