ABSTRACT
Lung cancer is the main reason of death associated with cancer universal. Screening of risky patients for low-dose CT scans for LC is currently being carried out in the United States and it is expected that other countries will follow shortly. Several lots of CT scans would have to be examined for CT lung cancer screening, which is a major challenge for radiologists. Therefore, the development of computational algorithms to improve screening is of great concern. The identification of pulmonary nodules which may or may not reflect early-stage lung cancer is a critical first stage in study of lung cancer screening CT scans. In medical image segmentation, deep, fully coevolutionary neural network (FCN) based architectures have demonstrated tremendous promise. These architectures, however, frequently have millions of parameters besides an insufficient no. of training models that lead to over-fitting and underprivileged generalization. we define a Fully convolutional Multi-scale Residual Dense Nets for Cardiac Segmentation and Diagnosis (Automated) of Cardiacvia classifiers (Ensemble)on other hand, we use JSRT dataset for prediction of lung cancer using x-ray images.
