Author/Authors :
Wu, Jia School of Computer Science and Engineering - Central South University - Changsha, China , Gou, Fangfang School of Computer Science and Engineering - Central South University - Changsha, China , Tan, Yanlin Second Xiangya Hospital of Central South University - Changsha, China
Abstract :
At present, human health is threatened by many diseases, and lung cancer is one of the most dangerous tumors that threaten
human life. In most developing countries, due to the large population and lack of medical resources, it is difficult for doctors to
meet patients’ needs for medical treatment only by relying on the manual diagnosis. Based on massive medical information, the
intelligent decision-making system has played a great role in assisting doctors in analyzing patients’ conditions, improving the
accuracy of clinical diagnosis, and reducing the workload of medical staff. This article is based on the data of 8,920 nonsmall cell
lung cancer patients collected by different medical systems in three hospitals in China. Based on the intelligent medical system,
on the basis of the intelligent medical system, this paper constructs a nonsmall cell lung cancer staging auxiliary diagnosis
model based on convolutional neural network (CNNSAD). CNNSAD converts patient medical records into word sequences,
uses convolutional neural networks to extract semantic features from patient medical records, and combines dynamic sampling
and transfer learning technology to construct a balanced data set. The experimental results show that the model is superior to
other methods in terms of accuracy, recall, and precision. When the number of samples reaches 3000, the accuracy of the
system will reach over 80%, which can effectively realize the auxiliary diagnosis of nonsmall cell lung cancer and combine
dynamic sampling and migration learning techniques to train nonsmall cell lung cancer staging auxiliary diagnosis models,
which can effectively achieve the auxiliary diagnosis of nonsmall cell lung cancer. The simulation results show that the model is
better than the other methods in the experiment in terms of accuracy, recall, and precision.
Keywords :
Cell , Auxiliary , CNNSAD , China