Block-Wise Neural Network for Brain Tumor Identification in Magnetic Resonance Images

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.