Combining Convolutional and Recurrent Neural Networks for Human Skin Detection
Sign Up to like & getrecommendations! Published in 2017 at "IEEE Signal Processing Letters"
DOI: 10.1109/lsp.2017.2654803
Abstract: Skin detection from images, typically used as a preprocessing step, has a wide range of applications such as dermatology diagnostics, human computer interaction designs, and etc. It is a challenging problem due to many factors… read more here.
Keywords: combining convolutional; detection; neural networks; human skin ... See more keywords
Combining convolutional sparse coding with total variation for sparse-view CT reconstruction.
Sign Up to like & getrecommendations! Published in 2022 at "Applied optics"
DOI: 10.1364/ao.445315
Abstract: Conventional dictionary-learning-based computed tomography (CT) reconstruction methods extract patches from an original image to train, ignoring the consistency of pixels in overlapping patches. To address the problem, this paper proposes a method combining convolutional sparse… read more here.
Keywords: total variation; convolutional sparse; sparse; sparse coding ... See more keywords
Ischemic stroke subtyping method combining convolutional neural network and radiomics
Sign Up to like & getrecommendations! Published in 2022 at "Journal of X-Ray Science and Technology"
DOI: 10.3233/xst-221284
Abstract: BACKGROUND: Cardiogenic embolism (CE) and large-artery atherosclerosis embolism (LAA) are the two most common ischemic stroke (IS) subtypes. OBJECTIVE: In order to assist doctors in the precise diagnosis and treatment of patients, this study proposed… read more here.
Keywords: subtyping method; method combining; combining convolutional; convolutional neural ... See more keywords