An Effective and Novel Approach for Brain Tumor Classification Using AlexNet CNN Feature Extractor and Multiple Eminent Machine Learning Classifiers in MRIs

A brain tumor is an uncontrolled malignant cell growth in the brain, which is denoted as one of the deadliest types of cancer in people of all ages. Early detection of brain tumors is needed to get proper and accurate treatment. Recently, deep learning technology has attained much attraction to the physicians for the diagnosis and treatment of brain tumors. This research presents a novel and effective brain tumor classification approach from MRIs utilizing AlexNet CNN for separating the dataset into training and test data along with extracting the features. The extracted features are then fed to BayesNet, sequential minimal optimization (SMO), Naive Bayes (NB), and random forest (RF) classifiers for classifying brain tumors as no-tumor, glioma, meningioma, and pituitary tumors. To evaluate our model's performance, we have utilized a publicly available Kaggle dataset. This paper demonstrates ROC, PRC, and cost curves for realizing classification performance of the models; also, performance evaluating parameters, such as accuracy, sensitivity, specificity, false positive rate, false negative rate, precision, f-measure, kappa statistics, MCC, ROC area, and PRC area, have been calculated for four testing options: the test data itself, cross-validation fold (CVF) 4, CVF 10, and percentage split (PS) 34% of the test data. We have achieved 88.75%, 98.15%, 86.25% and 100% of accuracy using the AlexNet CNN+BayesNet, AlexNet CNN+SMO, AlexNet CNN+NB, and AlexNet CNN+RF models, respectively, for the test data itself. The results imply that our approach is outstanding and very effective.