RT Journal Article
T1 Deep neural networks approaches for detecting and classifying colorectal polyps
A1 Nogueira Rodríguez, Alba
A1 Domínguez Carbajales, Rubén
A1 López Fernández, Hugo
A1 Iglesias, Águeda
A1 Cubiella Fernández, Joaquín
A1 Fernández Riverola, Florentino
A1 Reboiro Jato, Miguel
A1 González Peña, Daniel
K1 32 Ciencias Médicas
K1 1203.04 Inteligencia Artificial
K1 1203.20 Sistemas de Control Medico
AB Deep Learning (DL) has attracted a lot of attention in the field of medical image analysis because of its higher performance in image classification when compared to previous state-of-the-art techniques. In addition, a recent meta-analysis found that the diagnostic performance of DL models is equivalent to that of health-care professionals. In this scenario, a lot of research using DL for polyp detection and classification have been published showing promising results in the last five years. Our work aims to review the most relevant studies from a technical point of view, focusing on the low-level details for the implementation of the DL models. To do so, this review analyzes the published research covering aspects like DL architectures, training strategies, data augmentation, transfer learning, or the features of the datasets used and their impact on the performance of the models. Additionally, comparative tables summarizing the main aspects analyzed in this review are publicly available at https://github.com/sing-group/deep-learning-colonoscopy.
PB Neurocomputing
SN 09252312
YR 2021
FD 2021-01
LK http://hdl.handle.net/11093/2802
UL http://hdl.handle.net/11093/2802
LA eng
NO Neurocomputing, 423, 721-734 (2021)
NO Xunta de Galicia | Ref. ED431C2018 / 55-GRC
DS Investigo
RD 23-ene-2025