关键词: 自适应观测矩阵, 压缩感知, 自组织特征映射, 肿瘤细胞图像识别

Abstract:

Given the characteristics of serious cementation and information redundancy of gastric epithelium tumor cell images (hereinafter referred to as tumor cell images), we propose an algorithm which is a combination of the compressed sensing of  self-adaptive measurement (SAMCS) matrix and the selforganizing feature map (SOFM) neural network. Firstly, the tumor cell images are transferred to column vectors, then the linear observation vectors are generated through the SAM-CS theory. Finally, we train and classify the linear observation vectors by using the learning algorithm of SOFM neural network to implement the recognition of tumor cell images. Experimental results show that compared with traditional algorithms, the proposed algorithm has improved 4.2% of the recognition accuracy and 5.7% of the operation speed at least.

Key words: self-adaptive measurement matrix;compressed sensing;self-organizing feature map;the recognition of tumor cell images