Intelligent immune clonal optimization algorithm for pulmonary nodule classification

Qi Mao; Shuguang Zhao; Lijia Ren; Zhiwei Li; Dongbing Tong; Xing Yuan; Haibo Li; Qi Mao; Shuguang Zhao; Lijia Ren; Zhiwei Li; Dongbing Tong; Xing Yuan; Haibo Li

doi:10.3934/mbe.2021208

Mathematical Biosciences and Engineering

2021, Volume 18, Issue 4: 4146-4161. doi: 10.3934/mbe.2021208

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Intelligent immune clonal optimization algorithm for pulmonary nodule classification

1.
School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
2.
College of Information Science and Technology, Donghua University, Shanghai 201620, China
3.
ABB Electrical Machines Ltd

Academic Editor: Yu-Dong Zhang

Received: 15 March 2021 Accepted: 06 May 2021 Published: 12 May 2021

Computer-aided diagnosis (CAD) of pulmonary nodules is an effective approach for early detection of lung cancers, and pulmonary nodule classification is one of the key issues in the CAD system. However, CAD has the problems of low accuracy and high false-positive rate (FPR) on pulmonary nodule classification. To solve these problems, a novel method using intelligent immune clonal selection and classification algorithm is proposed and developed in this work. First, according to the mechanism and characteristics of chaotic motion with a logistic mapping, the proposed method utilizes the characteristics of chaotic motion and selects the control factor of the optimal chaotic state, to generate an initial population with randomness and ergodicity. The singleness problem of the initial population of the immune algorithm was solved by the proposed method. Second, considering on the characteristics of Gaussian mutation operator (GMO) with a small scale, and Cauchy mutation operator (CMO) with a big scale, an intelligent mutation strategy is developed, and a novel control factor of the mutation is designed. Therefore, a Gauss-Cauchy hybrid mutation operator is designed. Ultimately, in this study, the intelligent immune clonal optimization algorithm is proposed and developed for pulmonary nodule classification. To verify its accuracy, the proposed method was used to analyze 90 CT scans with 652 nodules. The experimental results revealed that the proposed method had an accuracy of 97.87% and produced 1.52 false positives per scan (FPs/scan), indicating that the proposed method has high accuracy and low FPR.
- computer-aided diagnosis (CAD),
- pulmonary nodule,
- immune clonal selective,
- feature selection,
- classification
Citation: Qi Mao, Shuguang Zhao, Lijia Ren, Zhiwei Li, Dongbing Tong, Xing Yuan, Haibo Li. Intelligent immune clonal optimization algorithm for pulmonary nodule classification[J]. Mathematical Biosciences and Engineering, 2021, 18(4): 4146-4161. doi: 10.3934/mbe.2021208

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Abstract

Computer-aided diagnosis (CAD) of pulmonary nodules is an effective approach for early detection of lung cancers, and pulmonary nodule classification is one of the key issues in the CAD system. However, CAD has the problems of low accuracy and high false-positive rate (FPR) on pulmonary nodule classification. To solve these problems, a novel method using intelligent immune clonal selection and classification algorithm is proposed and developed in this work. First, according to the mechanism and characteristics of chaotic motion with a logistic mapping, the proposed method utilizes the characteristics of chaotic motion and selects the control factor of the optimal chaotic state, to generate an initial population with randomness and ergodicity. The singleness problem of the initial population of the immune algorithm was solved by the proposed method. Second, considering on the characteristics of Gaussian mutation operator (GMO) with a small scale, and Cauchy mutation operator (CMO) with a big scale, an intelligent mutation strategy is developed, and a novel control factor of the mutation is designed. Therefore, a Gauss-Cauchy hybrid mutation operator is designed. Ultimately, in this study, the intelligent immune clonal optimization algorithm is proposed and developed for pulmonary nodule classification. To verify its accuracy, the proposed method was used to analyze 90 CT scans with 652 nodules. The experimental results revealed that the proposed method had an accuracy of 97.87% and produced 1.52 false positives per scan (FPs/scan), indicating that the proposed method has high accuracy and low FPR.

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