Lung Cancer Detection using VGG NET 16 Architecture

Abstract

Abstract Cancer is one of the main reason for loss of human life across the world. All the medical practitioners and researchers are dealing with the demanding situations to fight against cancer. Based on the report in 2019 from American Cancer Society, 96,480 deaths are anticipated due to skin cancers, 142,670 deaths are from lung cancers, 42,260 deaths are from breast cancers, 31,620 deaths are from prostate cancers, and 17,760 deaths are from mind cancers. Initial detection of most cancers has the pinnacle precedence for saving the lives. This paper proposed a lung cancer detection using Deep Learning based on VEE NET architecture. This was one of the famous models submitted to ILSVRC-2014. Visual checkup and manual practices are used on this venture for the various types of cancer diagnoses. This guide interpretation of scientific images that needs massive time intake and is notably susceptible to mistakes. Thus, in this project, we apply deep learning algorithms to identify lung cancer and its presence without the need for several consultations from different doctors. This leads to an earlier prediction of the presence of the disease and allows us to take prior actions immediately to avoid further consequences in an effective and cheap manner avoiding human error rate. In this project lung cancer and its presence is determined. A web application is developed as a hospital application where an input x-ray image is given to detect lung cancer.