Lung Cancer Detection and Classification from Chest CT Scans Using Machine Learning Techniques

Abstract

Lung cancer is one of the key causes of death amongst humans globally, with a mortality rate of approximately five million cases annually. The mortality rate is even higher than breast cancer and prostate cancer combination. However, early detection and diagnosis can improve the survival rate. Different modalities are used for lung cancer detection and diagnosis, while Computed Tomography (CT) scan images provide the most significant lung infections information. This research's main contribution is the detection and classification of different kinds of lung cancers such as Adenocarcinoma, Large cell carcinoma, and Squamous cell carcinoma. A novel lung cancer detection technique has been developed using machine learning techniques. The technique comprises feature extraction, fusion using patch base LBP (Local Binary Pattern) and discrete cosine transform (DCT). The machine learning technique such as support vector machine (SVM) and K-nearest neighbors (KNN) evaluated chest CT scan images dataset for texture feature classification. The proposed technique's performance achieves better accuracy of 93% and 91% for support vector machine and K-nearest neighbors, respectively, than state-of-the-art techniques.