TY  - CONF
AU  - Esmitt Ramirez
AU  - Carles Sanchez
AU  - Debora Gil
A2  - SYNASC
PY  - 2019//
TI  - Localizing Pulmonary Lesions Using Fuzzy Deep Learning
BT  - 21st International Symposium on Symbolic and Numeric Algorithms for Scientific Computing
SP  - 290
EP  - 294
N2  - 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 Computer-aided 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.
UR  - https://ieeexplore.ieee.org/abstract/document/9049881
L1  - http://refbase.cvc.uab.es/files/RSG2019.pdf
UR  - http://dx.doi.org/10.1109/SYNASC49474.2019.00048
N1  - IAM; 600.145; 600.140; 601.337; 601.323
ID  - Esmitt Ramirez2019
ER  -