Identification of Lung Cancer Using Convolutional Neural Networks Based Classification

Abstract

Identification of lung cancer is an efficient way to minimize the death rate and maximize survival rate of patients. It is an essential step to screen out the computed tomography (CT) images for pulmonary nodules towards the efficient treatment of lung cancer.  However, robust nodule identification and detection is a most critical task due the complexity of the surrounding environment and heterogeneity of the lung nodules. The use of machine learning to detect, predict, and classify disease has grown exponentially in the past few years, especially for complex tasks such as lung cancer detection and recognition. Deep Convolutional neural networks (DCNN) have exploded in popularity for transforming the field of computer vision research. In this paper, we are using Deep Convolutional Neural Network for lung cancer classification using CT images based lung cancer image dataset consortium (LIDC) for detecting  cancerous and noncancerous lung nodules for measuring the accuracy of classification better than existing methods.