Brain Tumor Prediction by analyzing MRI using deep learning architectures

Abstract

The brain tumor is a lethal illness that has endured innumerable individuals. Brain tumor causes abnormal growth of brain tissues, the tissues can be either malignant or non-malignant, but both are capable of causing long term harm and in about 95% cases can cause demise. Utilizing MRI (Magnetic resonance imaging) scans has become one of the meaningful techniques for identifying its existence in the human brain. Subsequent to getting the MRI filters these are physically investigated by experts to determine the presence of a brain tumor in a patient. Various specialists assessing MRI scans may result in outcomes that are not same; this happens because of the variance in forming evaluations from one professional to the next. Furthermore, because MRI scan analysis is a manual procedure, various people might make different mistakes. Based on the interpretations of the experts, two distinct MRI scans performed on the same patient may yield different findings. To make things simpler, reliable, and obtaining acquiring predictable outcomes for both specialists and non-specialists while performing assessment of MRI scans, this research work has presented deep learning strategies in the context of transfer learning models such as ResNet 50, ResNet 152 inception v3, VGG16 and also proposed Conv2d+SVM model to analyze MRI scans and determine the presence of a brain tumor. Also, this research work has utilized a dataset consisting of 253 images and then performed augmentation to increase the amount of data. After training, our model portrayed accuracy of 87.17% and 76.62% for ResNet 50, 99.28% and 88.24% for ResNet 152, 99.28% and 96.08% for inception v3, 99.78 and 86.27% for VGG16 and 92% and 78.3% for Conv2D+SVM in terms of training and validation respectively