High Accuracy Thyroid Tumor Image Recognition Based on Hybrid Multiple Models Optimization

With the development of computer vision recognition technology, more and more researchers apply this technology to the recognition of tumor images. But for cost reasons, many hospitals still use low-cost ultrasound and other cheap equipment, resulting in ambiguity, artifacts and many similar tumor noise areas images. Recent related studies have high precision in clear image recognition, such as face and number recognition in color images. However, they showed low accuracy and unstable results in ultrasonic image due to its fuzziness. The straight reason may be that many existing algorithms are not suitable for the fuzzy and noise image, and easily to misjudge the real objects and noise areas. In this paper, we proposed an approach based on R-CNN and RPN to distinguish the real objects from noise areas after data enhancement and morphological filtering with the optimization of a series of hyper-parameters and the combination of Color Doppler Flow Imaging (CDFI) blood flow signal model. We found that the key features of high-noise ultrasound images can be obtained quickly and accurately. From the experimental results, the accuracy and stability of our proposed method outperforms the state-of-the-art approaches on the real world thyroid tumor image data set.