A medium‐weight deep convolutional neural network‐based approach for onset epileptic seizures classification in EEG signals
Sign Up to like & getrecommendations! Published in 2022 at "Brain and Behavior"
DOI: 10.1002/brb3.2763
Abstract: Epileptic condition can be detected in EEG data seconds before it occurs, according to evidence. To overcome the related long‐term mortality and morbidity from epileptic seizures, it is critical to make an initial diagnosis, uncover… read more here.
Keywords: onset epileptic; deep convolutional; epileptic seizures; convolutional neural ... See more keywords
An improved deep convolutional neural network architecture for chromosome abnormality detection using hybrid optimization model
Sign Up to like & getrecommendations! Published in 2022 at "Microscopy Research and Technique"
DOI: 10.1002/jemt.24170
Abstract: Chromosomes are thread‐like structures located in the cell nucleus that contains the human body blueprint. Chromosome analysis is also known as karyotyping is the test taken to detect the abnormalities identified in the human chromosome.… read more here.
Keywords: methodology; dcnn architecture; deep convolutional; chromosome ... See more keywords
Deep convolutional neural network and 3D deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging
Sign Up to like & getrecommendations! Published in 2018 at "Magnetic Resonance in Medicine"
DOI: 10.1002/mrm.26841
Abstract: To describe and evaluate a new fully automated musculoskeletal tissue segmentation method using deep convolutional neural network (CNN) and three‐dimensional (3D) simplex deformable modeling to improve the accuracy and efficiency of cartilage and bone segmentation… read more here.
Keywords: segmentation; magnetic resonance; deep convolutional; convolutional neural ... See more keywords
PIV-DCNN: cascaded deep convolutional neural networks for particle image velocimetry
Sign Up to like & getrecommendations! Published in 2017 at "Experiments in Fluids"
DOI: 10.1007/s00348-017-2456-1
Abstract: Velocity estimation (extracting the displacement vector information) from the particle image pairs is of critical importance for particle image velocimetry. This problem is mostly transformed into finding the sub-pixel peak in a correlation map. To… read more here.
Keywords: particle image; image; image velocimetry; deep convolutional ... See more keywords
A deep convolutional neural network approach for predicting phenotypes from genotypes
Sign Up to like & getrecommendations! Published in 2018 at "Planta"
DOI: 10.1007/s00425-018-2976-9
Abstract: Main conclusionDeep learning is a promising technology to accurately select individuals with high phenotypic values based on genotypic data.AbstractGenomic selection (GS) is a promising breeding strategy by which the phenotypes of plant individuals are usually… read more here.
Keywords: deep convolutional; convolutional neural; phenotypes genotypes; approach ... See more keywords
Automatic method for classification of groundnut diseases using deep convolutional neural network
Sign Up to like & getrecommendations! Published in 2020 at "Soft Computing"
DOI: 10.1007/s00500-020-04946-0
Abstract: Groundnut is one of the most important and popular oilseed foods in the agricultural field, and its botanical name is Arachis hypogaea L. Approximately, the pod of mature groundnut contains 1–5 seeds with 57% of… read more here.
Keywords: classification; deep convolutional; method; convolutional neural ... See more keywords
Maize leaf disease classification using deep convolutional neural networks
Sign Up to like & getrecommendations! Published in 2019 at "Neural Computing and Applications"
DOI: 10.1007/s00521-019-04228-3
Abstract: Crop diseases are a major threat to food security. Identifying the diseases rapidly is still a difficult task in many parts of the world due to the lack of the necessary infrastructure. The accurate identification… read more here.
Keywords: classification; leaf disease; deep convolutional; convolutional neural ... See more keywords
CN-waterfall: a deep convolutional neural network for multimodal physiological affect detection
Sign Up to like & getrecommendations! Published in 2021 at "Neural Computing and Applications"
DOI: 10.1007/s00521-021-06516-3
Abstract: Affective computing solutions, in the literature, mainly rely on machine learning methods designed to accurately detect human affective states. Nevertheless, many of the proposed methods are based on handcrafted features, requiring sufficient expert knowledge in… read more here.
Keywords: neural network; affect detection; deep convolutional; convolutional neural ... See more keywords
Rapid Assessment of Acute Ischemic Stroke by Computed Tomography Using Deep Convolutional Neural Networks
Sign Up to like & getrecommendations! Published in 2021 at "Journal of Digital Imaging"
DOI: 10.1007/s10278-021-00457-y
Abstract: Acute stroke is one of the leading causes of disability and death worldwide. Regarding clinical diagnoses, a rapid and accurate procedure is necessary for patients suffering from acute stroke. This study proposes an automatic identification… read more here.
Keywords: ischemic stroke; using deep; deep convolutional; convolutional neural ... See more keywords
Evaluation of deep convolutional neural networks for glaucoma detection
Sign Up to like & getrecommendations! Published in 2019 at "Japanese Journal of Ophthalmology"
DOI: 10.1007/s10384-019-00659-6
Abstract: PurposeTo investigate the performance of deep convolutional neural networks (DCNNs) for glaucoma discrimination using color fundus imagesStudy designA retrospective studyPatients and methodsTo investigate the discriminative ability of 3 DCNNs, we used a total of 3312… read more here.
Keywords: image; discriminative ability; deep convolutional; convolutional neural ... See more keywords
Weighted pooling for image recognition of deep convolutional neural networks
Sign Up to like & getrecommendations! Published in 2018 at "Cluster Computing"
DOI: 10.1007/s10586-018-2165-4
Abstract: There are some traditional pooling methods in convolutional neural network, such as max-pooling, average pooling, stochastic pooling and so on, which determine the results of pooling based on the distribution of each activation in the… read more here.
Keywords: information; weighted pooling; pooling region; deep convolutional ... See more keywords