Statistical Feature-based Neural Network Approach for the Detection of Lung Cancer in Chest X-Ray Images K.

Lung cancer, if detected successfully at early stages, enables many treatment options,reduced risk of invasive surgery and increased survival rate. This paper presents a novelapproach to detect lung cancer from raw chest X-ray images. At the first stage, we use apipeline of image processing routines to remove noise and segment the lung from otheranatomical structures in the chest X-ray and extract regions that exhibit shape characteristicsof lung nodules. Subsequently, first and second order statistical texture features areconsidered as the inputs to train a neural network to verify whether a region extracted in thefirst stage is a nodule or not . The proposed approach detected nodules in the diseased areaof the lung with an accuracy of 96% using the pixel-based technique while the feature-basedtechnique produced an accuracy of 88%.