Hybrid model for brain tumor detection using convolution neural networks

Abstract

<div>The development of abnormal cells in the brain, some of which may turn out to be cancerous, is known as a brain tumor. Magnetic resonance imaging (MRI) is the most common technique for detecting brain tumors. Information about the abnormal tissue growth in the brain is visible from the MRI scans. In most research papers, machine learning (ML) and deep learning (DL) algorithms are applied to detect brain tumors. The radiologist can make speedy decisions because of this prediction. The proposed work creates a hybrid convolution neural networks (CNN) model and logistic regression (LR). The visual geometry group16 (VGG16) which was pre-trained model is used for the extraction of features. To reduce the complexity, we eliminated the last eight layers of VGG16. From this transformed model, the features are extracted in the form of a vector array. These features fed into different ML classifiers like support vector machine (SVM), and Naïve Bayes (NB), LR, extreme gradient boosting (XGBoost), AdaBoost, and random forest for training and testing. The performance of different classifiers is compared. The CNN-LR hybrid combination outperformed the remaining classifiers. The evaluation measures such as Recall, precision, F1-score, and accuracy of the proposed CNN-LR model are 94%, 94%, 94%, and 91% respectively.</div>