Enhanced Regularized Ensemble Encoderdecoder Network for Accurate Brain Tumor Segmentation.
Autoencoder
Brain tumor
Computer vision
MRI.
Medical imaging
Segmentation
Journal
Current medical imaging
ISSN: 1573-4056
Titre abrégé: Curr Med Imaging
Pays: United Arab Emirates
ID NLM: 101762461
Informations de publication
Date de publication:
2024
2024
Historique:
received:
04
09
2023
revised:
16
10
2023
accepted:
02
11
2023
medline:
23
2
2024
pubmed:
23
2
2024
entrez:
23
2
2024
Statut:
ppublish
Résumé
Segmenting tumors in MRI scans is a difficult and time-consuming task for radiologists. This is because tumors come in different shapes, sizes, and textures, making them hard to identify visually. This study proposes a new method called the enhanced regularized ensemble encoder-decoder network (EREEDN) for more accurate brain tumor segmentation. The EREEDN model first preprocesses the MRI data by normalizing the intensity levels. It then uses a series of autoencoder networks to segment the tumor. These autoencoder networks are trained using back-propagation and gradient descent. To prevent overfitting, the EREEDN model also uses L2 regularization and dropout mechanisms. The EREEDN model was evaluated on the BraTS 2020 dataset. It achieved high performance on various metrics, including accuracy, sensitivity, specificity, and dice coefficient score. The EREEDN model outperformed other methods on the BraTS 2020 dataset. The EREEDN model is a promising new method for brain tumor segmentation. It is more accurate and efficient than previous methods. Future studies will focus on improving the performance of the EREEDN model on complex tumors.
Sections du résumé
BACKGROUND
BACKGROUND
Segmenting tumors in MRI scans is a difficult and time-consuming task for radiologists. This is because tumors come in different shapes, sizes, and textures, making them hard to identify visually.
OBJECTIVE
OBJECTIVE
This study proposes a new method called the enhanced regularized ensemble encoder-decoder network (EREEDN) for more accurate brain tumor segmentation.
METHODS
METHODS
The EREEDN model first preprocesses the MRI data by normalizing the intensity levels. It then uses a series of autoencoder networks to segment the tumor. These autoencoder networks are trained using back-propagation and gradient descent. To prevent overfitting, the EREEDN model also uses L2 regularization and dropout mechanisms.
RESULTS
RESULTS
The EREEDN model was evaluated on the BraTS 2020 dataset. It achieved high performance on various metrics, including accuracy, sensitivity, specificity, and dice coefficient score. The EREEDN model outperformed other methods on the BraTS 2020 dataset.
CONCLUSION
CONCLUSIONS
The EREEDN model is a promising new method for brain tumor segmentation. It is more accurate and efficient than previous methods. Future studies will focus on improving the performance of the EREEDN model on complex tumors.
Identifiants
pubmed: 38389382
pii: CMIR-EPUB-138554
doi: 10.2174/0115734056275635231218114742
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1-17Subventions
Organisme : Deanship of Scientific Research, Najran University, Kingdom of Saudi Arabia
ID : NU/RG/MRC/12/10
Informations de copyright
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