Detection of Lung Nodules on X-ray Using Transfer Learning and Manual Features

Abstract

The well-established mortality rates due to lung cancers, scarcity of radiology experts and inter-observer variability underpin the dire need for robust and accurate computer aided diagnostics to provide a second opinion. To this end, we propose a feature grafting approach to classify lung cancer images from publicly available National Institute of Health (NIH) chest X-Ray dataset comprised of 30,805 unique patients. The performance of transfer learning with pre-trained VGG and Inception models is evaluated in comparison against manually extracted radiomics features added to convolutional neural network using custom layer. For classification with both approaches, Support Vectors Machines (SVM) are used. The results from the 5-fold cross validation report Area Under Curve (AUC) of 0.92 and accuracy of 96.87% in detecting lung nodules with the proposed method. This is a plausible improvement against the observed accuracy of transfer learning using Inception (79.87%). The specificity of all methods is >99%, however, the sensitivity of the proposed method (97.24%) surpasses that of transfer learning approaches (<67%). Furthermore, it is observed that the true positive rate with SVM is highest at the same false-positive rate in experiments amongst Random Forests, Decision Trees, and K-Nearest Neighbor classifiers. Hence, the proposed approach can be used in clinical and research environments to provide second opinions very close to the experts' intuition.