TY  - JOUR
T1  - An Intelligent System for Lung Cancer Diagnosis from Chest Radiographs
AU - , H. Khanna Nehemiah AU - , A. Kannan 
JO  - International Journal of Soft Computing
VL  - 1
IS  - 2
SP  - 133
EP  - 136
PY  - 2006
DA  - 2001/08/19
SN  - 1816-9503
DO  - ijscomp.2006.133.136
UR  - https://makhillpublications.co/view-article.php?doi=ijscomp.2006.133.136
KW  - ILCDS
KW  -nodule detection subsystem
KW  -nodule validation subsystem
KW  -neural networks
AB  - 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.
ER  -