Lung Cancer Classification and Through Deep Learning Model and Localization of Tumor

Abstract

Lung cancer a major threat of human life. Computer Tomography (CT) scan images are examined by the physician for identifying the cancer for patients. This examination when taken by the expertise can be able to identify the disease. When the tumor is small sometimes, the expertise may fail to notice and moreover, the manual examination is time consuming and considering the lack of experts, the artificial intelligence is widely used in the disease classification. When lung tumors is identified in earlier stages it can be diagnosed and increase the life of patients. In the artificial intelligence domain, deep learning algorithms perform accurately on disease classification from CT scan images. Deep Learning algorithms are widely used in health care domain. The purpose of this paper is to develop a prediction model based on deep learning for lung cancer as binary classification with normal and abnormal classes from CT scan images. The methodology followed involves image preprocessing, developing a trained model and use the model for application framework. The framework developed for lung cancer identification using Convolutional Neural Network (CNN) algorithm and this application gets CT scan image from user and classifies it. Experimental results show that the accuracy achieved with the proposed CNN algorithm is 97.10%.