Lung Cancer Detection using Ensemble of Machine Learning Models

Abstract

Lung cancer is a fatal genetic disease that has an abnormal growth of cancerous cells in the lungs of the human body. Since the lungs are one of the vital human body organs, lung cancer can have serious implications. In this work, we have focused on fast detection of lung cancer to be beneficial for patients and doctors. Lung cancer can be detected using Histopathology images and other diagnostic tools as well. The proposed work contains a hybridized model of Convolutional neural networks and an ensemble of Machine Learning algorithms: Support Vector Classifier, Random Forest, and XG Boost that detect the lung cancer using histopathology images. The overall accuracy achieved by this work is 99.13 %.