Data Engineering and Data Science

13Mining Heterogeneous Lung Cancer from Computer Tomography (CT) can with the Confusion Matrix

Denny Dominic* and Krishnan Balachandran

School of Computer Science and Engineering, Christ (Deemed to be University), Bengaluru, India

Abstract

Early detection of any sort of cancer, particularly lung cancer, which is one of the world’s most lethal illnesses, can save many lives. Life expectancy can be improved and the degree of mortality reduced by adopting the early forecast. While there are different methods like X-ray and CT scans to detect lung cancer cells, CT images resulted as more favored. The 2D images are used for more accurate medical results, such as CT scans. The proposed approach here will address how to interpret the CT images for the Mining Heterogeneous Lung Cancer from Computer Tomography (CT) Scan with the Confusion Matrix. This research will explore how the image conversion can be achieved through different methods of image processing to obtain better results from CT images. The Confusion Matrix helps to estimate inequality in a picture pattern. After the evaluation of the processed images by Confusion Matrix, a final accuracy with a result of 93% is obtained.

Keywords: Computer tomography (CT), LBP features, preprocessing, processing, smoothening

13.1 Introduction

Mining Heterogeneous Lung Cancer from Computer Tomography (CT) Scan with the Confusion Matrix is a part of research work carried on to detect lung cancer in the early stage. Here, multiple parameters ...