Title:Enhanced Regularized Ensemble Encoderdecoder Network for Accurate Brain
Tumor Segmentation
Volume: 20
Author(s): Abdullah A. Asiri, Ahmad Shaf*, Tariq Ali, Unza Shakeel, Muhammad Irfan, Saeed Alqahtani, Khlood M. Mehdar, Hanan T. Halawani, Ali H. Alghamdi, Abdullah Fahad A. Alshamrani and Aisha M. Mashraqi
Affiliation:
- Department of Computer Science, COMSATS University Islamabad, Sahiwal Campus, Sahiwal 57000, Pakistan
Keywords:
Medical imaging, Computer vision, Brain tumor, Segmentation, Autoencoder, MRI.
Abstract:
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:
This study proposes a new method called the enhanced regularized ensemble encoder-decoder network (EREEDN) for more accurate brain tumor
segmentation.
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:
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:
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.
