Random forest based lung nodule classification aided by clustering.
An automated lung nodule detection system can help spot lung abnormalities in CT lung images. Lung nodule detection can be achieved using template-based, segmentation-based, and classification-based methods. The existing systems that include a classification component in their structures have demonstrated better performances than their counterparts. Ensemble learners combine decisions of multiple classifiers to form an integrated output. To improve the performance of automated lung nodule detection, an ensemble classification aided by clustering (CAC) method is proposed. The method takes advantage of the random forest algorithm and offers a structure for a hybrid random forest based lung nodule classification aided by clustering. Several experiments are carried out involving the proposed method as well as two other existing methods. The parameters of the classifiers are varied to identify the best performing classifiers. The experiments are conducted using lung scans of 32 patients including 5721 images within which nodule locations are marked by expert radiologists. Overall, the best sensitivity of 98.33% and specificity of 97.11% have been recorded for proposed system. Also, a high receiver operating characteristic (ROC) A(z) of 0.9786 has been achieved.
Lee
S L A
SL
School of Engineering, Deakin University, Geelong, VIC 3217, Australia.
Kouzani
A Z
AZ
Hu
E J
EJ
D016000:Cluster Analysis; D006801:Humans; D008168:Lung / Q000000981:diagnostic imaging; D010363:Pattern Recognition, Automated; D011857:Radiographic Image Interpretation, Computer-Assisted; D012680:Sensitivity and Specificity; D003074:Solitary Pulmonary Nodule / Q000145:classification* / Q000000981:diagnostic imaging*; D014057:Tomography, X-Ray Computed