Share Email Print
cover

Proceedings Paper • new

Automated classification of histological subtypes of NSCLC using support vector machines with radiomic features
Author(s): Masahiro Yamada; Hidetaka Arimura; Kenta Ninomiya; Mazen Soufi
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Histological subtypes, i.e. adenocarcinoma (ADN) and squamous cell carcinoma (SCC), identified from a single biopsy occasionally differ from those from actual surgical resections in NSCLC. For increasing the classification accuracy, we aim to develop an automated approach for classifying histological subtypes of NSCLC using Gaussian, linear and polynomial support vector machines (SVMs) with radiomic features. Classification models of Gaussian, linear and polynomial SVMs constructed with radiomic features achieved the areas under the curves of 0.7542, 0.7522 and 0.7531, respectively. Histological subtypes of NSCLC could be classified into ADN and SCC using a Gaussian SVM with radiomic features.

Paper Details

Date Published: 27 March 2019
PDF: 4 pages
Proc. SPIE 11050, International Forum on Medical Imaging in Asia 2019, 110500P (27 March 2019); doi: 10.1117/12.2521511
Show Author Affiliations
Masahiro Yamada, Kyushu Univ. (Japan)
Hidetaka Arimura, Kyushu Univ. (Japan)
Kenta Ninomiya, Kyushu Univ. (Japan)
Mazen Soufi, Nara Institute of Science and Technology (Japan)


Published in SPIE Proceedings Vol. 11050:
International Forum on Medical Imaging in Asia 2019
Feng Lin; Hiroshi Fujita; Jong Hyo Kim, Editor(s)

© SPIE. Terms of Use
Back to Top