CAD for Two Class Classification of Lung Cancer using Statistical Features

Abstract

Lung Cancer causes maximum number of death in women and men all over the world. The Lung Cancer is divided into two types: Small Cell Lung Cancer (SCLC) and Non Small Cell Lung Cancer (NSCLC).A large number of techniques are being used for detection and diagnosis of Lung Cancer. The Computer Aided Diagnosis (CAD) is the most common and accurate technique for early detection of abnormal cells which can cause cancer to healthy lung tissues. CAD system works on the basis of analysis of condition of ultrasound images. CAD system follows different steps: Data collection (ultrasound image), Data Preprocessing (ROI Selection), Feature Extraction, Data Partitioning (hold- out method), Feature Classification and Result Calculation. To classify input ultrasound images into benign and malignant, different classifiers were used. The system work is based on the calculation of parameters such as individual accuracy, overall accuracy and sensitivity. These benchmarks are obtained by calculating the matrix of Support Vector Machine (SVM).The results were obtained by using various features using Statistical Methods. The best results achieved were having accuracy of 91.4% by using Gray Level Difference Statistics (GLDS). CAD system will use these results for detection of Lung Cancer cells in initial stage to enhance the capability of survival of patient. In this research work, the results obtained clearly demonstrate a promising accuracy and sensitivity of classes of lung cancer.