An Intelligent VGG-16 Framework for Brain Tumor Detection Using MRI-Scans

Although it is a difficult undertaking, using Convolutional Neural Networks (CNN) to detect brain tumors from MRI images has the potential to significantly improve patient outcomes. This article aims to properly diagnose various forms of brain tumor diseases by training a deep learning model with a huge dataset of MRI scans. A promising method in the realm of medical imaging is the VGG-16 model for the identification of brain tumors using MRI data. A popular deep convolutional neural network for image recognition applications is the VGG-16 which is proposed in this article. This system has performed remarkably well in identifying brain tumors from MRI data. The VGG-16 model can correctly diagnose many forms of brain cancers, including glioblastoma, meningioma, and pituitary tumors. It was trained on a sizable dataset of MRI images. Even when the tumors are tiny or situated in intricate regions of the brain, the model has demonstrated good sensitivity and specificity in identifying them. The VGG-16 model has a lot of potential for precise and early brain tumor diagnosis, which might improve patient health, and outcomes and perhaps save lives.