A Deep Learning Based Classification Model for the Detection of Brain Tumor using MRI

The diagnosis of a brain tumor requires high accuracy, as even small errors in judgment can lead to critical problems. For this reason, brain tumor segmentation is an important challenge for medical purposes. The wrong classification can lead to worse consequences. Therefore, these must be properly divided into many classes or levels, and this is where multiclass classification comes into play. The latest development of image classification technology has made great progress, and the most popular and better method is considered to be the best in this area is CNN, so this paper uses CNN for the brain tumor classification problem. The proposed model successfully classifies brain images into two distinct categories, namely the absence of tumors indicating that a given brain MRI is free of tumors or the Brain contains Tumor. This model produces an accuracy based on the results of a study that was conducted on a group of volunteers.