DETECTION OF LUNG CANCER USING SOFT COMPUTING TECHNIQUES

Sanjeev Indora; Dr. Dinesh Kr. Atal Kr. Atal; Supiksha Jain

doi:10.37591/rrjoi.v13i2.3221

Abstract

Cancer is a generic term that can affect any part of the body. Most cancers, 90%-95%, are because by environmental and lifestyle variables which cause genetic mutations. Inherited genetics account for 5% to 10% of the total. Early identification of lung cancer has improved patient survival and has been a vital study topic. It starts within the cells covering the bronchi and lung parts like bronchioles or alveoli. Due to the shape of cancer cells, early detection of lung cancer is challenging because most of the cells overlap. Imaging machines used to diagnose cancer within the body are X-Ray, CT, MRI, PET, SPECT, and transthoracic fine-needle aspiration. Various researchers have proposed lung cancer prediction models in the last few years, such as Feature fusion mechanism, Reference-model, 3D-CNN, Optical Flow Methods, Cloud-Based 3DDCNN CAD system, and likewise that is used to identify the nodules. The five processes of a lung cancer diagnosis include preprocessing, segmentation, feature extraction, feature selection, and classification. A comprehensive review of earlier research has been outlined in this paper to illustrate different methods and techniques for lung cancer detection, including their benefits and limitations