Brain Tumor Detection using Hybrid Neural Network based on VGGNet -19 and DenseNet

Gliomas are the most well-known and powerful of the cerebrum cancers, requiring a brief future in their most notable evaluation. In this approach, therapy planning is an important step in advancing the personal happiness of oncological patients. Magnetic resonance imaging (MRI) is a widely used imaging method for examining these tumors, however the large amount of data generated by MRI prevents manual division in a reasonable amount of time, limiting the use of accurate quantitative estimates in clinical practice. In this study, we propose using Convolution Neural Network Techniques to remove brain tumours from 2D Magnetic Resonance Brain Images (MRI). The experiment was conducted on a real-time dataset with a variety of tumour sizes, regions, forms, and intensities. The techniques involved are Alexnet, Densenet, Resnet and the other one is the combination of Vggnet 19 and Densenet. The detection of MRI brain tumours is difficult due to the wide diversity of injuries. In this paper, we present machine learning techniques for detecting malignancies in brain MRI that overcome the constraints of existing classifiers.