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

06/30/2020
by   Linde S. Hesse, et al.
4

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 <https://github.com/tueimage/lung-nodule-msc-2018>.

READ FULL TEXT

Authors

page 1

page 4

page 6

03/19/2018

Diagnostic Classification Of Lung Nodules Using 3D Neural Networks

Lung cancer is the leading cause of cancer-related death worldwide. Earl...
01/05/2020

Deep Transfer Convolutional Neural Network and Extreme Learning Machine for Lung Nodule Diagnosis on CT images

Diagnosis of benign-malignant nodules in the lung on Computed Tomography...
03/05/2019

Automatic Classification of Pathology Reports using TF-IDF Features

A Pathology report is arguably one of the most important documents in me...
10/08/2019

Lung nodule segmentation via level set machine learning

Lung cancer has the highest mortality rate of all cancers in both men an...
11/17/2021

Segmentation of Lung Tumor from CT Images using Deep Supervision

Lung cancer is a leading cause of death in most countries of the world. ...
05/21/2022

A Pilot Study of Relating MYCN-Gene Amplification with Neuroblastoma-Patient CT Scans

Neuroblastoma is one of the most common cancers in infants, and the init...
05/01/2021

MARL: Multimodal Attentional Representation Learning for Disease Prediction

Existing learning models often utilise CT-scan images to predict lung di...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.