Lung Nodule Classification on Computed Tomography Images Using Fractalnet

Lung Cancer is the most common cancer all over the world and is mostly diagnosed at later stages, thus increasing the risk of death. For early detection of malignant nodules there is a need for automated nodule detection system using imaging modalities like CT scan of lungs. Several machine learning algorithm specially deep learning algorithm have been used to accomplish this task, but without guaranteed accuracy. In the proposed work, we intend to improve the accuracy of pulmonary nodule classification system using fractalnet architecture. Fractalnet is also compared with other deep learning architectures and an elaborative discussion on the same is also mentioned in this paper. We have validated the classification of lung nodules on LUNA dataset and have achieved an accuracy, specificity, sensitivity, area under receiver operating characteristic curve score of 94.7%, 90.41%, 96.68%, 0.98 respectively using fractalnet architecture, which is a substantial improvement over previous works in literature.