Localizing Pulmonary Lesions Using Fuzzy Deep Learning
Ramírez, Esmitt (Centre de Visió per Computador (Bellaterra, Catalunya))
Sánchez Ramos, Carles (Universitat Autònoma de Barcelona. Departament de Ciències de la Computació)
Gil, Debora (Centre de Visió per Computador (Bellaterra, Catalunya))

Publicació:	Institute of Electrical and Electronics Engineers (IEEE), cop.2020
Descripció:	5 pag.
Resum:	The usage of medical images is part of the clinical daily in several healthcare centers around the world. Particularly, Computer Tomography (CT) images are an important key in the early detection of suspicious lung lesions. The CT image exploration allows the detection of lung lesions before any invasive procedure (e. g. bronchoscopy, biopsy). The effective localization of lesions is performed using different image processing and computer vision techniques. Lately, the usage of deep learning models into medical imaging from detection to prediction shown that is a powerful tool for Computeraided software. In this paper, we present an approach to localize pulmonary lung lesion using fuzzy deep learning. Our approach uses a simple convolutional neural network based using the LIDC-IDRI dataset. Each image is divided into patches associated a probability vector (fuzzy) according their belonging to anatomical structures on a CT. We showcase our approach as part of a full CAD system to exploration, planning, guiding and detection of pulmonary lesions.
Ajuts:	Agencia Estatal de Investigación RTI2018-095645-B-C21 Ministerio de Economía, Industria y Competitividad FIS-G64384969 Agència de Gestió d'Ajuts Universitaris i de Recerca 2017/SGR-1624 Ministerio de Economía, Industria y Competitividad BES-2016-078042 European Commission 712949
Drets:	Tots els drets reservats.
Llengua:	Anglès
Document:	Capítol de llibre ; Versió acceptada per publicar
Matèria:	Nodule detection ; Fuzzy detection ; Deep learning ; Lung cancer
Publicat a:	2019 21st International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), 2020, p. 290-294, ISBN 978-1-7281-5724-5

DOI: 10.1109/SYNASC49474.2019.00048

Postprint 6 p, 471.1 KB

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