Image processing techniques for analyzing CT scan images towards the early detection of lung cancer

Abstract

The application of image processing techniques for the analysis of CT scan images corresponding to lung cancer cells is gaining momentum in recent years. Therefore, it is of interest to discuss the use of a Computer-Aided Diagnosis (CAD) system using Computed Tomography (CT) images to help in the early diagnosis of lung cancer (to distinguish between benign and malignant tumors). We discuss and explore the design and significance of a CAD-CT image processed model in cancer diagnosis. Background: Small cell lung cancer and non-small cell lung cancer are common types of lung cancer [1]. The general symptoms of lung cancer include coughing up blood, chest pain, weight loss and loss of appetite, shortness of breath and feeling weak [2]. Early detection improves the survival rate from 15% to 50% [3]. However, there is a need to increase this survival rate more than the current value. Images generated by X-rays, Computed-Tomography (CT) scans, Magnetic Resonance Imaging (MRI) and others help in the early detection of lung cancer without surgery. The CT scan is the most recommended method which produces the 3D images of the lungs [3]. Mortality rate can be reduced by early detection and treatment of the disease. The process of early detection of cancer plays an important role to prevent cancer cells from multiplying and spreading. Existing lung cancer detection techniques are not adequate for providing accuracy. Hence, it is of importance to develop new methods for the early detection of lung cancer.