Research of Lung Cancer-Assisted Diagnosis
Algorithm Based on Multi-scale Convolution
Kernel Network
Yunfei Li
1,2 , Chuangang Li
1 , Yijia Cao
1 , Yue Zhao
1
,
and Zhiqiong Wang
1,3,4(&)
1 Sino-Dutch Biomedical and Information Engineer School, Northeastern
University, Shenyang, China
wangzq@bmie.neu.edu.cn
2 School of Computer Science and Engineering, Key Laboratory of Big Data
Management and Analytics (Liaoning), Northeastern University,
Shenyang, China
3 Neusoft Research of Intelligent Healthcare Technology, Co. Ltd.,
Shenyang, China
4 Acoustics Science and Technology Laboratory,
Harbin Engineering University, Harbin, China
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 computeraided 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.
Keywords: CT Á Lung cancer-aided diagnosis Á Image processing Á
Feature parameters Á Machine learning
© Springer Nature Singapore Pte Ltd. 2020
Q. Liang et al. (Eds.): Artificial Intelligence in China, LNEE 572, pp. 497–505, 2020.
https://doi.org/10.1007/978-981-15-0187-6_59
Algorithm Based on Multi-scale Convolution
Kernel Network
Yunfei Li
1,2 , Chuangang Li
1 , Yijia Cao
1 , Yue Zhao
1
,
and Zhiqiong Wang
1,3,4(&)
1 Sino-Dutch Biomedical and Information Engineer School, Northeastern
University, Shenyang, China
wangzq@bmie.neu.edu.cn
2 School of Computer Science and Engineering, Key Laboratory of Big Data
Management and Analytics (Liaoning), Northeastern University,
Shenyang, China
3 Neusoft Research of Intelligent Healthcare Technology, Co. Ltd.,
Shenyang, China
4 Acoustics Science and Technology Laboratory,
Harbin Engineering University, Harbin, China
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 computeraided 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.
Keywords: CT Á Lung cancer-aided diagnosis Á Image processing Á
Feature parameters Á Machine learning
© Springer Nature Singapore Pte Ltd. 2020
Q. Liang et al. (Eds.): Artificial Intelligence in China, LNEE 572, pp. 497–505, 2020.
https://doi.org/10.1007/978-981-15-0187-6_59