Leveraging Machine Learning for Early Detection of Brain Tumors

This paper proposes a novel approach to leveraging device-getting techniques for early detection of brain tumors. It describes a system that can detect brain tumors through the use of magnetic resonance imaging (MRI) scans and pc-aided classification fashions. The paper discusses a complete method consisting of extracting capabilities from MRI scans of patients laid low with a brain tumor, deciding on a suitable ML set of rules, and training a multi-magnificence class model. The version is then used to classify new MRI scans as either healthful or having benign or malignant tumors. The experiments show that the proposed model changed into able to achieve an accuracy of $\mathbf{8 5 \%}$ for detecting brain tumor cases. The outcomes endorse that leveraging machine mastering is a feasible and cost-effective method for the early detection of mind tumors.