Lung cancer classification using feed forward back propagation neural network for CT images

Manual computation of lung cancer is a time taking process. In the medical industry, software aided detection (SAD) aims to optimise the classification process. This paper proposes lung cancer detection for computed tomography (CT) images. It uses speed up robust feature (SURF) for feature extraction, genetic algorithm (GA) for feature optimisation and feed forward back propagation (FFBP), neural network (NN) for classification. The training mechanism utilises 200 cancerous images and the proposed method results in 96% classification accuracy and 94.7% sensitivity. This paper also discusses the possible future modifications in the presented work.