Lung Cancer Detection using Ensemble Techniques

Abstract

This paper implements a system for enhancing the detection of lung cancer through an ensemble approach, which amalgamates the predictive outputs generated by three distinct convolutional neural networks (CNNs): ResNet50, EfficientNet, and InceptionNet. Leveraging the diverse architectural features and learning capabilities of these CNNs, the ensemble method aims to synergistically fuse their individual predictions to achieve heightened accuracy and robustness in identifying potential lung cancer manifestations.