From Raw Pixels to Recurrence Image for Deep Learning of Benign and
Malignant Mediastinal Lymph Nodes on Computed Tomography
Abstract
Lung cancer causes the most cancer deaths worldwide and has one of the
lowest five-year survival rates of all cancer types. It is reported that
more than half of patients with lung cancer die within one year of being
diagnosed. Because mediastinal lymph node status is the most important
factor for the treatment and prognosis of lung cancer, the aim of this
study is to improve the predictive value in assessing the computed
tomography (CT) of mediastinal lymph-node malignancy in patients with
primary lung cancer. This paper introduces a new method for creating
pseudo-labeled images of CT regions of mediastinal lymph nodes by using
the concept of recurrence analysis in nonlinear dynamics for the
transfer learning. Pseudo-labeled images of original CT images are used
as input into deep-learning models. Three popular pretrained
convolutional neural networks (AlexNet, SqueezeNet, and DenseNet-201)
were used for the implementation of the proposed concept for the
classification of benign and malignant mediastinal lymph nodes using a
public CT database. In comparison with the use of the original CT data,
the results show the high performance of the transformed images for the
task of classification. The proposed method has the potential for
differentiating benign from malignant mediastinal lymph nodes on CT, and
may provide a new way for studying lung cancer using radiology imaging.