期刊
CMC-COMPUTERS MATERIALS & CONTINUA
卷 73, 期 3, 页码 5735-5753出版社
TECH SCIENCE PRESS
DOI: 10.32604/cmc.2022.031747
关键词
CNN; brain tumor; block-wise structure; VGG19; VGG16
资金
- Ministry of Education, Kingdom of Saudi Arabia under Najran University, Kingdom of Saudi Arabia [NU/IFC/ENT/01/014]
The precise diagnosis of brain tumors is crucial in the medical support for treating tumor patients. This paper proposes an improved computer-aided system using a fine-tuned Block-Wise Visual Geometry Group19 (BW-VGG19) architecture to enhance accuracy. The results demonstrate that the proposed method outperforms existing techniques.
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 computer-aided methods for early tumor detection. To remove this gap, we enhanced the computational power of a computer-aided system by proposing a fine-tuned 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.
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