Primary Tumor Origin Classification of Lung Nodules in Spectral CT using Transfer Learning

• URL: http://arxiv.org/abs/2006.16633v1
• Date: Tue, 30 Jun 2020 09:56:18 GMT
• Title: Primary Tumor Origin Classification of Lung Nodules in Spectral CT using Transfer Learning
• Authors: Linde S. Hesse, Pim A. de Jong, Josien P.W. Pluim, Veronika Cheplygina
• Abstract summary: Early detection of lung cancer has been proven to decrease mortality significantly. Recent development in computed tomography (CT), spectral CT, can potentially improve diagnostic accuracy. We propose a detection and classification system for lung nodules in CT scans.
• Score: 4.0657540412774935
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Early detection of lung cancer has been proven to decrease mortality significantly. A recent development in computed tomography (CT), spectral CT, can potentially improve diagnostic accuracy, as it yields more information per scan than regular CT. However, the shear workload involved with analyzing a large number of scans drives the need for automated diagnosis methods. Therefore, we propose a detection and classification system for lung nodules in CT scans. Furthermore, we want to observe whether spectral images can increase classifier performance. For the detection of nodules we trained a VGG-like 3D convolutional neural net (CNN). To obtain a primary tumor classifier for our dataset we pre-trained a 3D CNN with similar architecture on nodule malignancies of a large publicly available dataset, the LIDC-IDRI dataset. Subsequently we used this pre-trained network as feature extractor for the nodules in our dataset. The resulting feature vectors were classified into two (benign/malignant) and three (benign/primary lung cancer/metastases) classes using support vector machine (SVM). This classification was performed both on nodule- and scan-level. We obtained state-of-the art performance for detection and malignancy regression on the LIDC-IDRI database. Classification performance on our own dataset was higher for scan- than for nodule-level predictions. For the three-class scan-level classification we obtained an accuracy of 78\%. Spectral features did increase classifier performance, but not significantly. Our work suggests that a pre-trained feature extractor can be used as primary tumor origin classifier for lung nodules, eliminating the need for elaborate fine-tuning of a new network and large datasets. Code is available at \url{https://github.com/tueimage/lung-nodule-msc-2018}.

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