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In recent years, the incidence of thyroid nodules has shown an increasing trend year by year and has become one of the important diseases that endanger human health. Ultrasound medical images based on deep learning are widely used in clinical diagnosis due to their cheapness, no radiation, and low cost. The use of image processing technology to accurately segment the nodule area provides important auxiliary information for the doctor’s diagnosis, which is of great value for guiding clinical treatment. The purpose of this article is to explore the application value of combined detection of abnormal sugar-chain glycoprotein (TAP) and carcinoembryonic antigen (CEA) in the risk estimation of thyroid cancer in patients with thyroid nodules of type IV and above based on deep learning medical images. In this paper, ultrasound thyroid images are used as the research content, and the active contour level set method is used as the segmentation basis, and a segmentation algorithm for thyroid nodules is proposed. This paper takes ultrasound thyroid images as the research content, uses the active contour level set method as the basis of segmentation, and proposes an image segmentation algorithm Fast-SegNet based on deep learning, which extends the network model that was mainly used for thyroid medical image segmentation to more scenarios of the segmentation task. From January 2019 to October 2020, 400 patients with thyroid nodules of type IV and above were selected for physical examination and screening at the Health Management Center of our hospital, and they were diagnosed as thyroid cancer by pathological examination of thyroid nodules under B-ultrasound positioning. The detection rates of thyroid cancer in patients with thyroid nodules of type IV and above are compared; serum TAP and CEA levels are detected; PT-PCR is used to detect TTF-1, PTEN, and NIS expression; the detection, missed diagnosis, misdiagnosis rate, and diagnostic efficiency of the three detection methods are compared. This article uses the thyroid nodule region segmented based on deep learning medical images and compares experiments with CV model, LBF model, and DRLSE model. The experimental results show that the segmentation overlap rate of this method is as high as 98.4%, indicating that the algorithm proposed in this paper can more accurately extract the thyroid nodule area.

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The incidence rate of thyroid disease is increasing rapidly worldwide, and the number of thyroid patients is increasing. In this study, serum TAP (tumor abnormal protein) and CEA (carcinoembryonic antigen) were used to detect patients with thyroid nodules of class IV and above to explore the value of serum TAP combined detection of CEA in the risk assessment of thyroid cancer. In this paper, 400 patients with thyroid nodules above class IV diagnosed by physical examination in our hospital health management center from January 2019 to June 2021 were included in the study. Combined with the pathological test results, the patients were divided into risk groups. At the same time, different groups of serum TAP and CEA levels were detected by aggregation and electrochemiluminescence methods, and serum TAP and CEA levels were analyzed according to the pathological diagnostic indicators of CEA levels. The results showed that the levels of serum TAP and CEA in patients with thyroid cancer were significantly higher than those in patients with benign thyroid diseases, and the difference was statistically significant ( P  < 0.05). The sensitivity, specificity, and AUC under the ROC curve area of serum TAP were 85.25%, 85.06%, and 0.605, respectively. The sensitivity, specificity, and AUC under the ROC curve area of serum CEA were 89.85%, 88.00%, and 0.627, respectively. The sensitivity, specificity, and AUC under the ROC curve area of serum TAP combined with CEA were 96.84%, 96.79%, and 0.915, respectively. Therefore, the combined detection of serum TAP and CEA has a high early screening value in thyroid cancer.