Automated Lung Cancer Detection based on Multimodal Features Extracting Strategy Using Machine Learning Techniques

Lung Cancer is one of the leading causes of cancer-related deaths worldwide with minimal survival rate due to poor diagnostic system at the advanced cancer stage. In the past, researchers developed various tools in image processing to detect the Lung cancer of types as non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC) which are based on few features extracting methods. In this research, we extracted multimodal features such as texture, morphological, entropy based, scale invariant Fourier transform (SIFT), Ellipse Fourier Descriptors (EFDs) by considering multiple aspects and shapes morphologies. We then applied robust machine learning classification methods such as Naive Bayes, Decision Tree and Support Vector Machine (SVM) with its kernels such as Gaussian, Radial Base Function (RBF) and Polynomial. Jack-knife 10-fold cross validation was applied for training/ validation of data. The 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 curve (AUC). The highest detection accuracy was obtained with (TA=100%) with entropy, SIFT and texture features using Naive Bayes, texture features using SVM Polynomial. Moreover, the highest separation was obtained using entropy, morphological, SIFT and texture features with (AUC=1.00) using Naive Bayes classifier and texture features using Decision tree and SVM polynomial kernel.