Construction of a convolutional neural network classifier developed by computed tomography images for pancreatic cancer diagnosis

作者：Han Ma, Zhongxin Liu, Jingjing Zhang, Fengtian Wu, Chengfu Xu, Zhe Shen, Chaohui Yu, Youming Li · 发表于：World Journal of Gastroenterology · 年份：2020 · DOI：10.3748/wjg.v26.i34.5156 · 被引用次数：84 · 研究领域：Pancreatic and Hepatic Oncology Research、AI in cancer detection、Brain Tumor Detection and Classification

BACKGROUND: Efforts should be made to develop a deep-learning diagnosis system to distinguish pancreatic cancer from benign tissue due to the high morbidity of pancreatic cancer. AIM: To identify pancreatic cancer in computed tomography (CT) images automatically by constructing a convolutional neural network (CNN) classifier. METHODS: ., no cancer, cancer at tail/body, cancer at head/neck of the pancreas) using 10-fold cross validation, and measured the effectiveness of the model with regard to the accuracy, sensitivity, and specificity. RESULTS: < 0.001), with arterial phase having the highest sensitivity. CONCLUSION: We proposed a deep learning-based pancreatic cancer classifier trained on medium-sized datasets of CT images. It was suitable for screening purposes in pancreatic cancer detection.