Cargando…
Brain Tumor Classification Using a Combination of Variational Autoencoders and Generative Adversarial Networks
Brain tumors are a pernicious cancer with one of the lowest five-year survival rates. Neurologists often use magnetic resonance imaging (MRI) to diagnose the type of brain tumor. Automated computer-assisted tools can help them speed up the diagnosis process and reduce the burden on the health care s...
Autores principales: | Ahmad, Bilal, Sun, Jun, You, Qi, Palade, Vasile, Mao, Zhongjie |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8869455/ https://www.ncbi.nlm.nih.gov/pubmed/35203433 http://dx.doi.org/10.3390/biomedicines10020223 |
Ejemplares similares
-
Improving Skin Cancer Classification Using Heavy-Tailed Student T-Distribution in Generative Adversarial Networks (TED-GAN)
por: Ahmad, Bilal, et al.
Publicado: (2021) -
Adversarial and variational autoencoders improve metagenomic binning
por: Líndez, Pau Piera, et al.
Publicado: (2023) -
Sequential Variational Autoencoder with Adversarial Classifier for Video Disentanglement
por: Haga, Takeshi, et al.
Publicado: (2023) -
Molecular Generation for Desired Transcriptome Changes With Adversarial Autoencoders
por: Shayakhmetov, Rim, et al.
Publicado: (2020) -
Generative Adversarial Network-Based Scheme for Diagnosing Faults in Cyber-Physical Power Systems
por: Hassani, Hossein, et al.
Publicado: (2021)