A novel deep learning-based brain tumor detection using the Bagging ensemble with K-nearest neighbor
In the case of magnetic resonance imaging (MRI) imaging, image processing is crucial. In the medical industry, MRI images are commonly used to analyze and diagnose tumor growth in the body. A number of successful brain tumor identification and classification procedures have been developed by various...
Main Authors: | Archana K. V., Komarasamy G. |
---|---|
Format: | Article |
Language: | English |
Published: |
De Gruyter
2023-01-01
|
Series: | Journal of Intelligent Systems |
Subjects: | |
Online Access: | https://doi.org/10.1515/jisys-2022-0206 |
Similar Items
-
Automated Segmentation of Brain Tumor MRI Images Using Deep Learning
by: Surendran Rajendran, et al.
Published: (2023-01-01) -
An MRI brain tumor segmentation method based on improved U-Net
by: Jiajun Zhu, et al.
Published: (2024-01-01) -
U-Net Analysis Architecture For MRI Brain Tumor Segmentation
by: Nur Nafi'iyah
Published: (2023-11-01) -
MAU-Net: Mixed attention U-Net for MRI brain tumor segmentation
by: Yuqing Zhang, et al.
Published: (2023-11-01) -
PU-NET Deep Learning Architecture for Gliomas Brain Tumor Segmentation in Magnetic Resonance Images
by: Yamina Azzi, et al.
Published: (2023-11-01)