Random Forest based Classification Model for Lung Cancer Prediction on Computer Tomography Images

In recent days, lung cancer become a significant reason for the increased mortality rate. But, the identification and medical of the disease can considerably reduce the death rate. The usage of medical imaging especially Computer Tomography (CT) images are applied to diagnose the lung cancer and the physicians find it hard to understand and classify the cancer from the CT scan images. So, computer-based analysis tool has been developed for utilizing image processing approaches. In this paper, a new automated computer aided model has been developed to identify lung cancer on CT images. The presented model operates on four main stages namely pre-processing, segmentation, feature extraction and classification. For simulation processes, a benchmark dataset from Lung Image Database Consortium (LIDC) is applied and the results are investigated under several aspects. The attained experimental outcome clearly depicted that the presented model showed extraordinary classifier results on the applied dataset.