Affiliations: Department of Information Engineering, Hainan Vocational and Technical College, Haikou, Hainan, China
Correspondence:
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Corresponding author: Meng Qing, Department of Information Engineering, Hainan Vocational and Technical College, Haikou, Hainan 570216, China. E-mail: [email protected].
Abstract: BACKGROUND: Medical patients can be diagnosed early, however it is difficult to extract effective features in medical image segmentation based on semantic information. OBJECTIVE: A deep learning based image pixel block feature learning technology is studied in this paper. METHODS: The unlabeled image block sample training stack noise reduction automatic encoder is used to learn and extract the deep features of the image, and construct the initial depth neural network model. The labeled samples are used to fine-tune the initial depth neural network model, the deep features of the image correspond to the category, and the depth neural network model with classification function is obtained. The model is used to classify the pixel block samples in the segmented image and detect the initial segmentation region of brain tumor tissue. Finally, threshold segmentation and morphological methods are used to optimize the initial results to obtain accurate segmentation results of brain tumor tissue. RESULTS: The results show that this method can effectively improve the accuracy and sensitivity of segmentation. The running speed is also greatly improved compared with the traditional machine learning method.
Keywords: Deep learning, intelligent medical image, feature extraction method
