Brain Tumor MRI Classification using a Novel Deep Residual and Regional
CNN
- URL: http://arxiv.org/abs/2211.16571v1
- Date: Tue, 29 Nov 2022 20:14:13 GMT
- Title: Brain Tumor MRI Classification using a Novel Deep Residual and Regional
CNN
- Authors: Mirza Mumtaz Zahoor, Saddam Hussain Khan
- Abstract summary: A novel deep residual and regional-based Res-BRNet Convolutional Neural Network (CNN) is developed for effective brain tumor (Magnetic Resonance Imaging) MRI classification.
The efficiency of the developed Res-BRNet is evaluated on a standard dataset; collected from Kaggle and Figshare containing various tumor categories.
Experiments prove that the developed Res-BRNet outperforms the standard CNN models and attained excellent performances.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Brain tumor classification is crucial for clinical analysis and an effective
treatment plan to cure patients. Deep learning models help radiologists to
accurately and efficiently analyze tumors without manual intervention. However,
brain tumor analysis is challenging because of its complex structure, texture,
size, location, and appearance. Therefore, a novel deep residual and
regional-based Res-BRNet Convolutional Neural Network (CNN) is developed for
effective brain tumor (Magnetic Resonance Imaging) MRI classification. The
developed Res-BRNet employed Regional and boundary-based operations in a
systematic order within the modified spatial and residual blocks. Moreover, the
spatial block extract homogeneity and boundary-defined features at the abstract
level. Furthermore, the residual blocks employed at the target level
significantly learn local and global texture variations of different classes of
brain tumors. The efficiency of the developed Res-BRNet is evaluated on a
standard dataset; collected from Kaggle and Figshare containing various tumor
categories, including meningioma, glioma, pituitary, and healthy images.
Experiments prove that the developed Res-BRNet outperforms the standard CNN
models and attained excellent performances (accuracy: 98.22%, sensitivity:
0.9811, F-score: 0.9841, and precision: 0.9822) on challenging datasets.
Additionally, the performance of the proposed Res-BRNet indicates a strong
potential for medical image-based disease analyses.
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