Efficient segmentation and classification of lung cancer Diagnosis Techniques Using Ct Images: A Review

Abstract

Cancer is an illness caused by uncontrolled division of eccentric cells in any a part of body. Cancer is at the highest of the few places on the list of fatal diseases and is present worldwide, however it continues to rise. Most of the cases associate early detection of lung cancer is cumbersome. This research aims to present an effective and efficient CAD method of computer-aided diagnosis for the classification of lung cancer. Automatic identification and classification of lung infection through computer tomography (CT) images provides an enormous probable to supplement the conventional healthcare approach for tackling the lung cancers. In this research, various research papers are analysed and discussed on the different techniques used for identification and detection of lung disease on CT image. The lung organ scanned output CT image may be affected due to various external noises such as salt and pepper noise, random noise, speckle noise and Gaussian noise. The adaptive 2D filtering algorithms are applied to restore the lung CT image. The low quality CT lung image is enhanced as high quality CT lung image in terms contrast and brightness using various image enhancement techniques. The CT image lung infection disease region is properly segmented using various clustering and threshold techniques for extracting Region of Interest (ROI). The ROI is the disease portion on the image. The feature extraction techniques are used to calculate different features for doing classification further. The Machine Learning (ML), Deep Learning (DL) and Artificial Neural Network (ANN) are applied for classifying different stages of lung CT disease image