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Brain tumors analysis is important in timely diagnosis and effectivetreatment to cure patients. Tumor analysis is challenging because of tumormorphology like size, location, texture, and heteromorphic appearance in themedical images. In this regard, a novel two-phase deep learning-based frameworkis proposed to detect and categorize brain tumors in magnetic resonance images(MRIs). In the first phase, a novel deep boosted features and ensembleclassifiers (DBF-EC) scheme is proposed to detect tumor MRI images from healthyindividuals effectively. The deep boosted feature space is achieved through thecustomized and well-performing deep convolutional neural networks (CNNs), andconsequently, fed into the ensemble of machine learning (ML) classifiers. Whilein the second phase, a new hybrid features fusion-based brain tumorclassification approach is proposed, comprised of dynamic-static feature and MLclassifier to categorize different tumor types. The dynamic features areextracted from the proposed BRAIN-RENet CNN, which carefully learnsheteromorphic and inconsistent behavior of various tumors, while the staticfeatures are extracted using HOG. The effectiveness of the proposed two-phasebrain tumor analysis framework is validated on two standard benchmark datasets;collected from Kaggle and Figshare containing different types of tumor,including glioma, meningioma, pituitary, and normal images. Experimentalresults proved that the proposed DBF-EC detection scheme outperforms andachieved accuracy (99.56%), precision (0.9991), recall (0.9899), F1-Score(0.9945), MCC (0.9892), and AUC-PR (0.9990). While the classification scheme,the joint employment of the deep features fusion of proposed BRAIN-RENet andHOG features improves performance significantly in terms of recall (0.9913),precision (0.9906), F1-Score (0.9909), and accuracy (99.20%) on diversedatasets.

A New Deep Hybrid Boosted and Ensemble Learning-based Brain Tumor Analysis using MRI