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 patients. Manual segmentation cancer analysis from many Magnetic Resonance Images (MRIs) created practice problematic timewasting task experts. As result, there necessity more accurate computer-aided methods early detection. To remove this gap, we enhanced computational power of system by proposing fine-tuned Block-Wise Visual Geometry Group19 (BW-VGG19) architecture. In method, pre-trained VGG19 with CNN architecture block-wise mechanism to enhance system`s accuracy. publicly accessible Contrast-Enhanced Imaging (CE-MRI) dataset collected 2005 2020 different hospitals China has been used research. Our proposed method simple achieved an accuracy 0.98%. We compare our technique results existing Convolutional Neural network (CNN), VGG16, approaches. indicate that outperforms best associated methods.