  Brain tumors analysis is important in timely diagnosis and effective
treatment to cure patients. Tumor analysis is challenging because of tumor
morphology like size, location, texture, and heteromorphic appearance in the
medical images. In this regard, a novel two-phase deep learning-based framework
is proposed to detect and categorize brain tumors in magnetic resonance images
(MRIs). In the first phase, a novel deep boosted features and ensemble
classifiers (DBF-EC) scheme is proposed to detect tumor MRI images from healthy
individuals effectively. The deep boosted feature space is achieved through the
customized and well-performing deep convolutional neural networks (CNNs), and
consequently, fed into the ensemble of machine learning (ML) classifiers. While
in the second phase, a new hybrid features fusion-based brain tumor
classification approach is proposed, comprised of dynamic-static feature and ML
classifier to categorize different tumor types. The dynamic features are
extracted from the proposed BRAIN-RENet CNN, which carefully learns
heteromorphic and inconsistent behavior of various tumors, while the static
features are extracted using HOG. The effectiveness of the proposed two-phase
brain 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. Experimental
results proved that the proposed DBF-EC detection scheme outperforms and
achieved 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 and
HOG features improves performance significantly in terms of recall (0.9913),
precision (0.9906), F1-Score (0.9909), and accuracy (99.20%) on diverse
datasets.
