CAJ | 학술논문

为了提高孤立性肺结节良恶性诊断中的分类准确度，提出了一个基于自生成神经网络的自动分类算法。该算法首先对PET／CT 图像进行去噪、配准等预处理，分别提取孤立性肺结节的结构影像特征和代谢特征，然后对自生成神经网络进行训练和优化，构建分类器，根据距离测度和自动连接规则对待分类肺结节进行分类。初步的实验结果表明，与传统的自生成神经网络算法和BP神经网络算法相比，改进的自生成神经网络分类算法能得到更高的分类准确率。
위료제고고립성폐결절량악성진단중적분류준학도，제출료일개기우자생성신경망락적자동분류산법。해산법수선대PET／CT 도상진행거조、배준등예처리，분별제취고립성폐결절적결구영상특정화대사특정，연후대자생성신경망락진행훈련화우화，구건분류기，근거거리측도화자동련접규칙대대분류폐결절진행분류。초보적실험결과표명，여전통적자생성신경망락산법화BP신경망락산법상비，개진적자생성신경망락분류산법능득도경고적분류준학솔。
To improve the classification accuracy of diagnosis of solitary pulmonary nodules , this paper proposes an automatic classification algorithm based on the self‐generating neural net‐works .The algorithm first deploys preprocessing methods on PET/CT image ,such as denoising and registration .Then ,structural and metabolic features of solitary pulmonary nodules are extrac‐ted separately .After that ,the improved self‐generating neural network is trained and a classifier is built to classify unspecified samples on the basis of distance measure and automatic connection . Experimental results show that compared with traditional self‐generating neural networks and BP neural network algorithm ,the improved self‐generating neural networks algorithm can guarantee higher classification accuracy .