Results 161 to 170 of about 3,882,996 (203)

Convolutional neural networks in brain disease diagnosis: a unified review of Alzheimer's, Parkinson's, and brain tumor classification. [PDF]

Front Neurosci
Nathea R, Ghosh K, Nisha JS.
europepmc   +1 more source

Rapid detection of urinary tract infection by analyzing the dried spots of biological fluids using convolutional neural networks. [PDF]

Sci Rep
Bordbar MM, Gh FNM, Sheini A, Alborz M, Halabian R, Pargar A, Khoshsafar H, Baydemir G, Samadinia H, Bagheri H.   +9 more
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Impact of Transfer Learning on Convolutional Neural Networks for Odontogenic Tumor Diagnosis. [PDF]

Head Neck Pathol
Giraldo-Roldán D, Nakamura TCR, Claret AF, Dos Santos GC, Pulido-Díaz K, Gerber-Mora R, Gonzalez-Perez LV, Câmara J, Pontes HAR, Martin MD, Oliveira MC, Prado VP, Silveira FM, Bologna-Molina R, Araújo ALD, Moraes MC, Vargas PA.   +16 more
europepmc   +1 more source

Brain Tumor Classification in MRI Images Using Combined Transfer Learning and Convolutional Neural Networks. [PDF]

J Imaging
Abbas M, Hassan M, Wang RZ, Teng CH.
europepmc   +1 more source

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Factorized Convolutional Neural Networks

2017 IEEE International Conference on Computer Vision Workshops (ICCVW), 2017
In this paper, we propose to factorize the convolutional layer to reduce its computation. The 3D convolution operation in a convolutional layer can be considered as performing spatial convolution in each channel and linear projection across channels simultaneously.
Wang, Min, Liu, Baoyuan, Foroosh, Hassan
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Attentiondrop for Convolutional Neural Networks

2019 IEEE International Conference on Multimedia and Expo (ICME), 2019
Dropout has been widely used in fully connected networks but becomes less effective for convolutional neural networks (CNNs), since the spatially correlated features still allow dropped information to flow through the network. To make dropout more practical for CNNs, structured dropout methods have been recently proposed by dropping regions with fixed ...
Zhihao Ouyang, Yan Feng, Zihao He, Tianbo Hao, Tao Dai 0001, Shu-Tao Xia   +5 more
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Denoising Convolutional Neural Network

2015 IEEE International Conference on Information and Automation, 2015
Convolutional Neural Network (CNN) is a kind of deep artificial neural network. CNN has kinds of merits, such as multidimensional data input, and fewer parameters. However, the network always has the problem of overfitting due to lots of connection in the full connection layer.
Qingyang Xu, Chengjin Zhang, Li Zhang 0043   +2 more
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Restricted Convolutional Neural Networks

Neural Processing Letters, 2018
In this paper, a new type of convolutional neural network is proposed which is inspired by cellular automata research. This model is referred to as “restricted convolutional neural network” and its characteristic is that the feature maps are not fully connected, i.e.
Mehran Mirkhan, Mohammad Reza Meybodi
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In vitro convolutional neural networks

Nature Machine Intelligence, 2022
© 2022 Springer Nature Limited. Published 11 July 2022. The author declares no competing interests.
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