Detection system for lung cancer based on neural network: X-Ray validation performance

In our daily life, cancer is well-known disease that causes of death in both men and women and understand about the survival rate of lung cancer which is extremely poor. To increase this survival rate of cancerous patient, it is primarily to detect at premature stage which enables many new options for the cancer treatment without risk. In this paper, the author represents Lung Cancer Detection System for finding of lung cancer by analyzing chest X-rays with the help of image processing mechanisms. This system assists to radiologists for their X-ray image interpretation 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 techniques to denoise, to enhance, for segmentation and edge detection in the X-ray image to extract the area, perimeter and shape of nodule. These extracted features are considered as the inputs of neural network to train and to verify whether the extracted nodule is a malignant or non-malignant. 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 are processed in three stages to attain more quality and accuracy in the processed examination.