Authors : H. Khanna Nehemiah and A. Kannan
Abstract: In this study we propose an Intelligent Lung Cancer Diagnosis System (ILCDS) that has been developed to detect all possible lung nodules from chest radiographs. Our system uses image processing techniques and feed forward neural networks for detection and validation of nodules. Nodules are relatively low-contrast white circular objects within the lung fields. As nodules are the most common sign of lung cancer, nodule detection in chest radiographs is a major diagnostic problem. Even experienced radiologists have trouble while distinguishing the normal pattern of blood vessels and nodules that indicate the Lung cancer. Our work is centered around two major sub systems namely Nodule Detection Subsystem (NDS) and Nodule Validation Subsystem (NVS). The Nodule Detection Subsystem is constructed using wavelet based image-processing techniques such as Besov ball projections, Laplacian of Gaussian filter and Gabor wavelet networks which are used to remove the noise from the image, find the edges of the image and detect the nodule, size and its location. The NDS detects all the possible nodules and gives the nodule-detected image. The processed image shows all nodules in the chest radiograph. Since all nodules are not cancerous, the nodules detected by the NDS are validated by the NVS. The NVS is constructed using Feed forward neural network classifiers, which classifies the nodules into non-cancerous and cancerous nodules.
H. Khanna Nehemiah and A. Kannan , 2006. An Intelligent System for Lung Cancer Diagnosis from Chest Radiographs. International Journal of Soft Computing, 1: 133-136.