Lung Carcinoma Detection Techniques: A Survey

摘要

Diagnosis of Lung carcinoma (lung cancer) at an early stage will reduce the death rate that happens by lung carcinoma. Medical imaging techniques are used to detect the tumor by human or by computer such as Computed Tomography (CT). Due to a great number of Computed Tomography (CT) scan images, a fast and accurate diagnosis was difficult for radiologists. Therefore, the demand for Computer Aid Diagnosis (CAD) for Lung Cancer was increasing. To resolve this problem various Machine Learning and Deep Learning techniques are used such as Support Vector Machine (SVM), Convolutional Neural Network (CNN), Random Forest, and so on. 3 Dimensional Convolutional Neural Network (3D CNN) works better than 2 Dimensional Convolutional Neural Network (2D CNN). Here, surveyed such techniques applied to different datasets like X-radiation (X-ray) images, Lung Image Database Consortium (LIDC/IDRI) dataset, Lung Nodule Analysis 2016 (LUNA16) dataset, etc. to detect lung carcinoma.