Convolutional Neural Network based Framework for Automatic Lung Cancer Detection from Lung CT Images

Abstract

Lung cancer is one of the major causes of death across the globe. Medical interventions with modern healthcare facilities are widely used to cure lung cancer. However, it is indispensable to have research on early detection of lung cancer as it has potential to save lives of people. With innovations in machine learning Computer Aided Design (CAD) systems for automatic detection of lung cancer has become an important solution. Particularly deep learning models such as Convolutional Neural Network (CNN) is found to have necessary mechanisms to learn features from Computed Tomography (CT) scan images and detect the probability of lung cancer. In this paper, we propose a CNN based model for automatic detection of lung cancer provided lung CT scan image. We proposed an algorithm known as CNN based Automatic Lung Cancer Detection (CNN-ALCD) which is based on supervised learning phenomenon. The learned model is capable of detecting lung cancer from any newly arrived test sample. The proposed solution has different mechanisms such as preprocessing, building CNN with different layers, training the CNN model and performing lung cancer detection. Empirical study revealed that the proposed CNN based model outperforms many existing neural network-based methods with highest accuracy 94.11%. Therefore, the proposed system can be integrated with a Clinical Decision Support System (CDSS) in healthcare units for automatic diagnosis of lung cancer.