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Computer-aided lung nodule recognition by SVM classifier based on combination of random undersampling and SMOTE.

Sui Y, Wei Y, Zhao D - Comput Math Methods Med (2015)

Bottom Line: However, problems of unbalanced datasets often have detrimental effects on the performance of classification.Eight features including 2D and 3D features are extracted for training and classification.Experimental results show that for different sizes of training datasets our RU-SMOTE-SVM classifier gets the highest classification accuracy among the four kinds of classifiers, and the average classification accuracy is more than 92.94%.

View Article: PubMed Central - PubMed

Affiliation: Software College, Northeastern University, Shenyang 110004, China.

ABSTRACT
In lung cancer computer-aided detection/diagnosis (CAD) systems, classification of regions of interest (ROI) is often used to detect/diagnose lung nodule accurately. However, problems of unbalanced datasets often have detrimental effects on the performance of classification. In this paper, both minority and majority classes are resampled to increase the generalization ability. We propose a novel SVM classifier combined with random undersampling (RU) and SMOTE for lung nodule recognition. The combinations of the two resampling methods not only achieve a balanced training samples but also remove noise and duplicate information in the training sample and retain useful information to improve the effective data utilization, hence improving performance of SVM algorithm for pulmonary nodules classification under the unbalanced data. Eight features including 2D and 3D features are extracted for training and classification. Experimental results show that for different sizes of training datasets our RU-SMOTE-SVM classifier gets the highest classification accuracy among the four kinds of classifiers, and the average classification accuracy is more than 92.94%.

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Related in: MedlinePlus

Illustration of rmin⁡ and rmax⁡.
© Copyright Policy - open-access
Related In: Results  -  Collection


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fig9: Illustration of rmin⁡ and rmax⁡.

Mentions: Elongation measures the elongation or asymmetry degree of an object. It is calculated through (A.2), where rmin⁡ is the measurement from the centroid to the nearest point on the boundary, while rmax⁡ is the measurement from the centroid to the farthest point on the boundary, as illustrated in Figure 9. One has(A.2)Elongation=rmin⁡rmax⁡.


Computer-aided lung nodule recognition by SVM classifier based on combination of random undersampling and SMOTE.

Sui Y, Wei Y, Zhao D - Comput Math Methods Med (2015)

Illustration of rmin⁡ and rmax⁡.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4419492&req=5

fig9: Illustration of rmin⁡ and rmax⁡.
Mentions: Elongation measures the elongation or asymmetry degree of an object. It is calculated through (A.2), where rmin⁡ is the measurement from the centroid to the nearest point on the boundary, while rmax⁡ is the measurement from the centroid to the farthest point on the boundary, as illustrated in Figure 9. One has(A.2)Elongation=rmin⁡rmax⁡.

Bottom Line: However, problems of unbalanced datasets often have detrimental effects on the performance of classification.Eight features including 2D and 3D features are extracted for training and classification.Experimental results show that for different sizes of training datasets our RU-SMOTE-SVM classifier gets the highest classification accuracy among the four kinds of classifiers, and the average classification accuracy is more than 92.94%.

View Article: PubMed Central - PubMed

Affiliation: Software College, Northeastern University, Shenyang 110004, China.

ABSTRACT
In lung cancer computer-aided detection/diagnosis (CAD) systems, classification of regions of interest (ROI) is often used to detect/diagnose lung nodule accurately. However, problems of unbalanced datasets often have detrimental effects on the performance of classification. In this paper, both minority and majority classes are resampled to increase the generalization ability. We propose a novel SVM classifier combined with random undersampling (RU) and SMOTE for lung nodule recognition. The combinations of the two resampling methods not only achieve a balanced training samples but also remove noise and duplicate information in the training sample and retain useful information to improve the effective data utilization, hence improving performance of SVM algorithm for pulmonary nodules classification under the unbalanced data. Eight features including 2D and 3D features are extracted for training and classification. Experimental results show that for different sizes of training datasets our RU-SMOTE-SVM classifier gets the highest classification accuracy among the four kinds of classifiers, and the average classification accuracy is more than 92.94%.

Show MeSH
Related in: MedlinePlus