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. Three pretrained convolutional neural networks that are AlexNet, SqueezeNet, and DenseNet201 were trained and tested with the transformed images. Classification accuracies and areas under the receiver operating characteristic curve obtained from the ten-fold cross-validation are 93% and 0.97, 96% and 0.99, and 100% and 1 for the SqueezeNet, AlexNet, and DenseNet201, respectively. 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.
Highlights
Lung cancer tends to reach to lymph nodes before it extends to other parts of the body
EXPERIMENTAL RESULTS Recurrence images of the original computed tomography (CT) data of benign and malignant mediastinal lymph nodes were used for classification with the AlexNet, SqueezeNet, and DenseNet
Parameters for the transfer training of these three networks have been described in the foregoing subsection II-C (Transfer learning from pretrained convolutional neural network (CNN))
Summary
Lung cancer tends to reach to lymph nodes before it extends to other parts of the body. After the diagnosis of lung cancer, it is important to discover if the tumor has grown to adjacent (regional) lymph nodes or distant sites. If the cancer has progressed beyond regional lymph nodes and is found in distant ones or other tissues, the patient is considered to develop metastasis [1]. It is necessary to identify the types of lymph nodes affected by the cancer so that cancer staging can be determined and how it should be treated to reduce the risk of recurrence [2]. Computed tomography (CT) is a common diagnostic imaging test [4] to determine which lymph nodes are affected by the cancer so that a suitable treatment of the disease can be recommended [5].
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