Stage Classification of Lung Cancer using the Comparative Analysis of the Machine Learning Techniques

Abstract

Stage classification today is widely used in the fields of biological sciences and engineering, The idea of the stage classification is to perform a clinical analysis of the spread of the disease . Lung cancer which is termed as Lung Carcinoma is a highly dangerous lung tumour which is defined by the uncontrolled growth of cells in the tissues of the lung. This growth of cells leading to the tumour identifies the different stages of cancer. The tumours are identified based on the probability density function. The goal is to design models for the stage classification of the of cancer patients . The description of the extent of a tumour consists of three components: T for extent of the primary tumour, N for involvement of lymph nodes, and M for distant metastases. Each T, N, and M component is divided into several categories (eg, T1, T2). This study proposes to build a classification system that can identify the stage classification using the lung cancer dataset for better accuracy. An “IQ-OTHNCCD” lung cancer dataset of 1190 images representing CT scan slices of 110 cases is used in this research.