Paper Info
Title
Evaluation Of The Degree Of Malignancy Of Lung Nodules In Computed Tomography Images
Abstract
In lung cancer diagnosis, the design of robust Computer Aided Diagnosis (CAD) systems needs to include an adequate differentiation of benign from malignant nodules. This paper presents a CAD system for the classification of lung nodules in chest Computed Tomography (CT) scans as the way to diagnose lung cancer. The proposed method measures a set of 295 heterogeneous characteristics, including morphology, intensity or texture features, that were used as input of different KNN and SVM classifiers.The system was modeled and trained using a groundtruth provided by specialists taken from a public lung image dataset, the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI). This image dataset includes chest CT scans with lung nodule location together with information about the degree of malignancy, among other properties, provided by multiple expert clinicians. In particular, the computed degree of malignancy try to follow the manual labeling by the different radiologists. Promising results were obtained with a first order SVM with an exponential kernel achieving an area under the receiver operating characteristic curve of 96.2 +/- 0.5% when compared with the groundtruth provided in the public CT lung image dataset.
Year
DOI
Venue
2017
10.5220/0006116200740080
PROCEEDINGS OF THE 12TH INTERNATIONAL JOINT CONFERENCE ON COMPUTER VISION, IMAGING AND COMPUTER GRAPHICS THEORY AND APPLICATIONS (VISIGRAPP 2017), VOL 6
Keywords
Field
DocType
Medical Diagnostic Imaging, Computer-aided Diagnosis, Computed Tomography, Machine Learning, Feature Extraction
Computer vision,Lung,Computer science,Malignancy,Artificial intelligence,Computed tomography,Radiology
Conference
Citations 
PageRank 
References 
0
0.34
0
Authors
4
Name
Order
Citations
PageRank
Luís Gonçalves1456.19
J Novo28918.73
A. Cunha322.76
Aurélio J. C. Campilho432140.49