MASK CLASSIFICATION SEGMENTATION METHOD BASED ON GROUPED CONVOLUTION AND SPATIAL PYRAMIDAL CONVOLUTION MODEL FOR THYROID CANCER IDENTIFICATION

Abstract

Ultrasound plays different roles in the whole process of thyroid cancer management. With the advancement of ultrasound imaging technology and diagnostic level, it is gradually becoming an irreplaceable role in the diagnosis and treatment of thyroid cancer. However, the diagnosis of thyroid cancer by means of sonographic features is subjective and highly dependent on the operator’s experience and knowledge. To avoid unnecessary puncture biopsies, alleviate anxiety and improve diagnostic efficiency, in this paper, we utilize a purely convolutional model with a pyramid structure strategy to extract sonographic features at different scales. Combined with a per-pixel classification segmentation method, which is different from the previous mainstream, it is used for the intelligent recognition of thyroid cancer. Finally, the experimental results show that our radiologists achieve better performance than five mainstream segmentation methods in four metrics (Sensitivity, Jarccard, Dice, ASD) on the thyroid cancer dataset. It provides the possibility to help radiologists to overcome diagnostic subjectivity and obtain accurate, reproducible and more objective diagnostic results.