Cervical Cancer Detection And Classification Using Independent Level Sets And Multi SVMs

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F16%3APU120282" target="_blank" >RIV/00216305:26220/16:PU120282 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/7760935" target="_blank" >https://ieeexplore.ieee.org/document/7760935</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TSP.2016.7760935" target="_blank" >10.1109/TSP.2016.7760935</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Cervical Cancer Detection And Classification Using Independent Level Sets And Multi SVMs

Popis výsledku v původním jazyce

Introduced in 1940, Pap smear test has proven to be an effective screening method to determine the different stages of cervical cancer. Identification and classification of Pap smear images to detect cervical cancer via manual screening is a challenging task for pathologists therefore increasing the chances of human error. In this paper, we propose an automatic method to detect and classify the grade of cervical cancer using both geometric and texture features of Pap smear images and classifying accordingly using multi SVM. The geometric features are obtained through segmentation of nucleus and cytoplasm using independent level sets, detecting whether the cell is cancerous or normal, with reference to the ground truth. By extracting well defined GLCM texture features and using a combination of PCA and the best class of multi SVM, the images are classified with an accuracy of 95%.

Název v anglickém jazyce

Cervical Cancer Detection And Classification Using Independent Level Sets And Multi SVMs

Popis výsledku anglicky

Introduced in 1940, Pap smear test has proven to be an effective screening method to determine the different stages of cervical cancer. Identification and classification of Pap smear images to detect cervical cancer via manual screening is a challenging task for pathologists therefore increasing the chances of human error. In this paper, we propose an automatic method to detect and classify the grade of cervical cancer using both geometric and texture features of Pap smear images and classifying accordingly using multi SVM. The geometric features are obtained through segmentation of nucleus and cytoplasm using independent level sets, detecting whether the cell is cancerous or normal, with reference to the ground truth. By extracting well defined GLCM texture features and using a combination of PCA and the best class of multi SVM, the images are classified with an accuracy of 95%.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20201 - Electrical and electronic engineering

Projekt

<a href="/cs/project/LO1401" target="_blank" >LO1401: Interdisciplinární výzkum bezdrátových technologií</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2016

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 39th International Conference on Telecommunication and Signal Processing, TSP 2016

ISBN

978-1-5090-1287-9

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

523-528

Název nakladatele

Neuveden

Místo vydání

Neuveden

Místo konání akce

Vídeň

Datum konání akce

27. 6. 2016

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000390164000114