Abstract
BACKGROUND: In order to explore the regulation of quality of life and immune function in patients with thyroid cancer after radiotherapy, a method based on deep learning technology was proposed. A deep learning detection method for thyroid cancer is proposed. METHODS: It mainly includes three main modules: data preprocessing, thyroid cancer regional detection module, and thyroid cancer benign and malignant classification module. The data set in the experiment comes from LIDC-IDRI and is processed by the data preprocessing module to generate a standard data format that can be processed by the framework. The treatment of thyroid cancer can help patients relapse malignant thyroid cancer and prevent recurrence in advance. RESULTS: The results showed that most patients are diagnosed because of obvious swelling of local thyroid mass and conscious compression symptoms in the neck. At this time, they often miss the best treatment time, so as to reduce the surgical effect. CONCLUSIONS: The metastasis and invasion of cancer cells are fast, the cancerous lesions are easy to form adhesion with the surrounding tracheal tissue, and the cancer cells invade the surrounding soft tissue, which is also easy to cause the cancerous tissue not to be completely removed. Clinical Trial Registration. Therefore, deep learning technology is used to treat residual cancerous lesions to ensure the surgical effect.