Abstract
In recent years, the number of patients with lung cancer has risen steadily, becoming the first malignant tumor in men and the second malignant tumor in women. Researchers at home and abroad have found that pulmonary nodule-assisted diagnosis can detect pulmonary nodules early and effectively reduce the risk of lung cancer. Therefore, deep learning has become a new hotspot in the diagnosis of pulmonary nodules. The research content of this paper is as follows: In this paper, we extract features of lung nodules with geometric features, gray value features, texture features and use support vector machine (SVM) and extreme learning machine (ELM) to train and classify the lung nodules. The convolutional neural network (CNN) deep learning method was used to extract the features of CT images of lung nodules, to establish a characteristic model of CT images of pulmonary nodules, and to classify the benign and malignant lung nodules. This paper presents a method for computer-aided diagnosis of pulmonary nodules based on improved CNN. This method uses the convolutional neural network (CNN) to extract the features of CT images of lung nodules and establishes the feature model of CT images of pulmonary nodules. The multi-scale convolution kernel depth learning is used to prove the advancement of improved algorithms.
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Acknowledgements
This research was partially supported by the National Natural Science Foundation of China under Grant Nos. 61472069, 61402089 and U1401256, the China Postdoctoral Science Foundation under Grant Nos. 2019T120216 and 2018M641705, the Fundamental Research Funds for the Central Universities under Grant Nos. N161602003, N180408019 and N180101028, the Open Program of Neusoft Institute of Intelligent Healthcare Technology, Co. Ltd. under Grant No. NIMRIOP1802, and the fund of Acoustics Science and Technology Laboratory.
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Li, Y., Li, C., Cao, Y., Zhao, Y., Wang, Z. (2020). Research of Lung Cancer-Assisted Diagnosis Algorithm Based on Multi-scale Convolution Kernel Network. In: Liang, Q., Wang, W., Mu, J., Liu, X., Na, Z., Chen, B. (eds) Artificial Intelligence in China. Lecture Notes in Electrical Engineering, vol 572. Springer, Singapore. https://doi.org/10.1007/978-981-15-0187-6_59
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DOI: https://doi.org/10.1007/978-981-15-0187-6_59
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