Construction of a convolutional neural network classifier developed by computed tomography images for pancreatic cancer diagnosis
Han Ma, Zhongxin Liu, Jingjing Zhang, Fengtian Wu, Chengfu Xu, Zhe Shen, Chaohui Yu, Youming Li
Abstract
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.