Abstract
Objective: accurately classifying the malignancy of lesions detected in a screening scan is critical for reducing false positives. Radiomics holds great potential to differentiate malignant from benign tumors by extracting and analyzing a large number of quantitative image features. Since not all radiomic features contribute to an effective classifying model, selecting an optimal feature subset is critical. Methods: this work proposes a new multi-objective based feature selection (MO-FS) algorithm that considers sensitivity and specificity simultaneously as the objective functions during feature selection. For MO-FS, we developed a modified entropy-based termination criterion that stops the algorithm automatically rather than relying on a preset number of generations. We also designed a solution selection methodology for multi-objective learning that uses the evidential reasoning approach (SMOLER) to automatically select the optimal solution from the Pareto-optimal set. Furthermore, we developed an adaptive mutation operation to generate the mutation probability in MO-FS automatically. Results: we evaluated the MO-FS for classifying lung nodule malignancy in low-dose CT and breast lesion malignancy in digital breast tomosynthesis. Conclusion: the experimental results demonstrated that the feature set selected by MO-FS achieved better classification performance than features selected by other commonly used methods. Significance: the proposed method is general and more effective radiomic feature selection strategy.
Original language | English (US) |
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Article number | 8654586 |
Pages (from-to) | 194-204 |
Number of pages | 11 |
Journal | IEEE Journal of Biomedical and Health Informatics |
Volume | 24 |
Issue number | 1 |
DOIs | |
State | Published - Jan 2020 |
Keywords
- Radiomics
- evidential reasoning
- feature selection
- lesion malignancy classification
- multi-objective evolutionary algorithm
ASJC Scopus subject areas
- Health Information Management
- Health Informatics
- Electrical and Electronic Engineering
- Computer Science Applications