Abstract:
Lung cancer is the most common cause of death due to cancer in both men and women throughout the world. Statistics from the American Cancer Society estimated that in 2014 about 224,000 new cases of lung cancer in the U.S. occurred and about 159,000 deaths were due to the disease. According to the U.S. National Cancer Institute, approximately one out of every 14 men and women in the U.S. will be diagnosed with cancer of the lung at some point in their lifetime.
Pulmonary nodules can be an indication for primary lung cancer , CT offers better contrast , detect smaller, earlier stage nodules with a higher sensitivity .
CAD is technologies to improve the quality and productivity of radiologists’ tasks by improving the accuracy and consistency of radiological diagnoses and also by reducing the image reading time and miss.
The objective of this study is identifying all nodules from the chest CT lung images and classifying these nodules into tumor and non-tumor nodules, to reduce the false positive rate using Image processing and Neural Network techniques.
The results obtained from proposed CAD system are good compared to existing CAD systems. The sensitivity and specificity of the image were 100% , 96% respectively and achieved accuracy of 98.2%.
