5179
Block-Wise Neural Network for Brain Tumor Identification in Magnetic Resonance Images
Fawaz Alqahtani1 and Abdullah A. Asiri1
1Najran University, Najran, Saudi Arabia
1Najran University, Najran, Saudi Arabia
Synopsis
Motivation: medical support for treating tumor patients
Goal(s): critical necessity for more accurate computeraided methods for early tumor detection
Approach: proposing a finetuned Block-Wise Visual Geometry Group19 (BW-VGG19) architecture
Results: We achieved an accuracy of 0.98%
Impact: the best results associated with the existing methods
Abstract
The precise brain tumor diagnosis is critical and shows a vital role in the medical support for treating tumor patients. Manual brain tumor segmentation for cancer analysis from many Magnetic Resonance Images (MRIs) created in medical practice is a problematic and timewasting task for experts. As a result, there is a critical necessity for more accurate computeraided methods for early tumor detection. To remove this gap, we enhanced the computational power of a computer-aided system by proposing a finetuned Block-Wise Visual Geometry Group19 (BW-VGG19) architecture. In this method, a pre-trained VGG19 is fine-tuned with CNN architecture in the block-wise mechanism to enhance the system`s accuracy. The publicly accessible Contrast-Enhanced Magnetic Resonance Imaging (CE-MRI) dataset collected from 2005 to 2020 from different hospitals in China has been used in this research. Our proposed method is simple and achieved an accuracy of 0.98%. We compare our technique results with the existing Convolutional Neural network (CNN), VGG16, and VGG19 approaches. The results indicate that our proposed technique outperforms the best results associated with the existing methods.Acknowledgements
Authors would like to acknowledge the support of the Deputy for Research and Innovation- Ministry of Education, Kingdom of Saudi Arabia for funding this research through a project (NU/IFC/ENT/01/014) under the institutional funding committee at Najran University, Kingdom of Saudi Arabia.References
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Figures
Sample images of contrast-enhanced MRI dataset
DOI: https://doi.org/10.58530/2024/5179