期刊
MATHEMATICAL BIOSCIENCES AND ENGINEERING
卷 18, 期 4, 页码 4146-4161出版社
AMER INST MATHEMATICAL SCIENCES-AIMS
DOI: 10.3934/mbe.2021208
关键词
computer-aided diagnosis (CAD); pulmonary nodule; immune clonal selective; feature selection; classification
资金
- National Natural Science Foundation of China [61673257, 61705127]
- construction project of applied undergraduate of Shanghai for the major of electrical engineering and automation of SUES
- doctoral training foundation of SUES [0232A30100210931]
A novel approach using intelligent immune clonal selection and classification algorithm for pulmonary nodule CAD system was proposed and developed in this study. Experimental results showed that the proposed method had high accuracy and low FPR.
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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