Benign and Malignant Classification Model of Pulmonary Nodules Based on Residual Neural Network

Zhenzhe Lin, Guitang Wang, Qinshen Fu, Guozhen Wang
<span title="">2019</span> <i title="Atlantis Press"> Proceedings of the 2019 International Conference on Big Data, Electronics and Communication Engineering (BDECE 2019) </i> &nbsp; <span class="release-stage">unpublished</span>
Computer-assisted diagnosis is of significance in the timely treatment of lung cancer with classifying benign and malignant pulmonary nodules. Aiming at improving the low accuracy rate of benign and malignant pulmonary nodules and reducing the misdiagnosis rate and wrong-diagnosis rate in computer-aided diagnosis system, a classification model of pulmonary nodules based on residual network was proposed. Firstly, selected some lung CT images from LIDC-IDRI as a data set, amplified the data by
more &raquo; ... izontal flipping, and then converted them into single channel images. After cropping and normalization, the data was finally divided into training set and test set (7:3), and used to train and test a residual network (ResNet-26). After training, test results represent that the model accuracy rate, sensitivity and specificity are 97.53%, 97.91% and 97.18%. By comparing various methods, the raised method performs better than others according to accuracy, sensitivity and specificity, which demonstrates that it has the ability to help doctors in diagnosis.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="">doi:10.2991/acsr.k.191223.038</a> <a target="_blank" rel="external noopener" href="">fatcat:h4bhfwpij5fu5la77drmroluna</a> </span>
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