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@article{159206,
        author = {Poonam Jain and Mithilesh Vishwakarma},
        title = {ULTRASOUND NERVE SEGMENTATION},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {9},
        number = {11},
        pages = {540-544},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=159206},
        abstract = {The medical industry has given us incredible, previously unimagined facilities today. Accurate ultrasonic nerve segmentation has drawn a lot of interest since it helps ensure the effectiveness of regional anesthesia, minimize surgical damage, and hasten the healing process after surgery. However, accurate brain ultrasound segmentation is challenging to accomplish because to the features of excessive noise and low contrast in ultrasonic pictures. One significant drawback of these noise is that it might be challenging for medical professionals to pinpoint the precise nerve in which to administer anesthesia. Using CNN and U-Net, we were able to recognize nerve structures in ultrasound pictures. Determine the image's presence or absence of the disease and its context by reducing the size of the image.},
        keywords = {Machine learning; Python; Ultrasound images; Nerve segmentation},
        month = {},
        }