Segmentation of Medical Image Based on Superpixel Boundary Perceptual Convolutional Network in Cancer Diagnosis and Treatment

In order to reduce the computational complexity of breast tumor segmentation algorithms and improve the accuracy of breast segmentation, this paper proposes a breast tumor segmentation method based on super pixel boundary perceptual convolutional network. This method first uses super pixel segmentation convolutional network algorithm to segment breast medical images, and then uses region growth algorithm to achieve breast tumor segmentation at super pixel level. The research results show that in the classification of breast tumors, the fusion efficiency based on the classifier level is better than the fusion based on the feature set; the index R proposed and adopted in this paper can effectively select the appropriate individual classifier and generate a better performing integration 06%. Classifier, the accuracy of this classifier is 88.73%, the sensitivity is 97.06%. The method can be used to assist doctors in breast cancer diagnosis, improve the efficiency and accuracy of doctors' work diagnosis, and has certain significance for clinical research and large-scale screening of breast cancer.