Neural Network Based Approach for Detection of Abnormal Regions of Lung Cancer in X-Ray Image

Cancer is the most familiar disease that affect both men and women. The survival rate of lung cancer is extremely poor. To increase the survival rate of cancer patient, it is essential to detect it very early stage which enables many treatment options with reduced risk. Now a days, the image processing mechanisms are used in a number of medical profession for improving detection of lung cancer. This paper presents a neural network based approach to detect lung cancer from raw chest X-ray images. The author use an image processing technique to remove noise using various filters and segment the lung to detect abnormal regions in the X-ray image and extracted regions that demonstrate area, perimeter and shape characteristics of lung nodules. These shape features are considered as the inputs to train a neural network and to verify whether a region is a malignant nodule or not. This research work concentrate on detecting nodules, early stages of cancer diseases, appearing in patient’s lungs. Most of the nodules can be observed after carefully selection of parameters. The training dataset of X-ray images of lung cancer are processed in three stages to attain more quality and accuracy in the observational results.