Segmentation of MR images for brain tumor detection using autoencoder neural network
Abstract Medical images often require segmenting into different regions in the first analysis stage. Relevant features are selected to differentiate various regions from each other, and the images are segmented into meaningful (anatomically significant) regions based on these features. The purpose o...
Main Authors: | Farnaz Hoseini, Shohreh Shamlou, Milad Ahmadi-Gharehtoragh |
---|---|
Formato: | Artigo |
Idioma: | English |
Publicado em: |
Springer
2024-10-01
|
Colecção: | Discover Artificial Intelligence |
Assuntos: | |
Acesso em linha: | https://doi.org/10.1007/s44163-024-00180-x |
Registos relacionados
-
FIESTA: Autoencoders for accurate fiber segmentation in tractography
Por: Félix Dumais, et al.
Publicado em: (2023-10-01) -
ASD-SAENet: A Sparse Autoencoder, and Deep-Neural Network Model for Detecting Autism Spectrum Disorder (ASD) Using fMRI Data
Por: Fahad Almuqhim, et al.
Publicado em: (2021-04-01) -
A Novel Generative Adversarial Network-Based Approach for Automated Brain Tumour Segmentation
Por: Roohi Sille, et al.
Publicado em: (2023-01-01) -
Super-resolution reconstruction of brain magnetic resonance images via lightweight autoencoder
Por: J. Andrew, et al.
Publicado em: (2021-01-01) -
Deep Learning for Skeleton-Based Human Activity Segmentation: An Autoencoder Approach
Por: Md Amran Hossen, et al.
Publicado em: (2024-06-01)