Machine learning-based lungs cancer detection using reconstruction independent component analysis and sparse filter features

Abstract

In the present study, based on the multivariate nature of images from lung cancer, we extracted autoencoder, reconstruction independent component analysis (RICA) and sparse filters features along with traditional texture and morphological features. The machine learning techniques such as Support Vector Machine (SVM) with Gaussian, radial base function (RBF) and polynomial kernels, decision tree and Naïve Bayes were used classification purpose. The Jackknife 10-fold cross validation (CV) was used for training/testing data formulation and performance was evaluated in terms of sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), total accuracy (TA), false positive rate (FPR) and area under the receiving operating curve (AUC) to distinguish the small cell lung cancer (SCLC) from non-small cell lung cancer (NSCLC) images. The highest detection performance was obtained by extracting RICA and Sparse filters with 100% of sensitivity, specificity, PPV, NPV and TA using SVM with RBF, polynomial kernels and Naïve Bayes followed by Sparse filter with decision tree with TA (99.78%). The highest AUC of 1.00 was obtained by extracting RICA and Sparse filters using decision tree, SVM Gaussian, RBF and polynomial and Naïve Bayes. The results showed the good potential of these methods for lung cancer detection (LCD).

KEYWORDS:

• Imaging
• nonlinear media
• non-small cell lung cancer (NSCLC)
• small cell lung cancer (SCLC)
• reconstruction independent component analysis (RICA)
• machine learning
• classification

Disclosure statement

No potential conflict of interest was reported by the author(s).