Abstract
The thyroid gland, one of the largest endocrine organs, plays a key role in regulating metabolism. Early detection of thyroid diseases significantly lowers mortality rates.Diagnosing these diseases traditionally relies on the expertise of radiologists and pathologists, but this paper demonstrates that deep learning techniques offer a promising solution for automatic detection.The study introduces a novel approach that uses two pre-operative medical imaging techniques to classify different thyroid conditions, including normal, thyroiditis, cystic, and cancer. Utilizing a cutting -edge CNN, the model achieved high accuracy-0.972 for ultrasound images and 0.942 for CT scans. These result show the potential of CNN models in medical imaging, suggesting they could be used more broadly in clinical setting.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
