Performance analysis of various machine learning-based approaches for detection and classification of lung cancer in humans

Abstract

Lung cancer is one of the most common causes of death among all cancer-related diseases (Cancer Research UK in Cancer mortality for common cancers. http://www.cancerresearchuk.org/health-professional/cancer-statistics/mortality/commoncancers- compared, 2017). It is primarily diagnosed by performing a scan analysis of the patient’s lung. This scan analysis could be of X-ray, CT scan, or MRI. Automated classification of lung cancer is one of the difficult tasks, attributing to the varying mechanisms used for imaging patient’s lungs. Image processing and machine learning approaches have shown a great potential for detection and classification of lung cancer. In this paper, we have demonstrated effective approach for detection and classification of lung cancer-related CT scan images into benign and malignant category. Proposed approach firstly processes these images using image processing techniques, and then further supervised learning algorithms are used for their classification. Here, we have extracted texture features along with statistical features and supplied various extracted features to classifiers. We have used seven different classifiers known as k-nearest neighbors classifier, support vector machine classifier, decision tree classifier, multinomial naive Bayes classifier, stochastic gradient descent classifier, random forest classifier, and multi-layer perceptron (MLP) classifier. We have used dataset of 15750 clinical images consisting of both 6910 benign and 8840 malignant lung cancer related images to train and test these classifiers. In the obtained results, it is found that accuracy of MLP classifier is higher with value of 88.55% in comparison with the other classifiers.