A framework for in-vivo human brain tumor detection using image augmentation and hybrid features.

Brain tumor is caused by the uncontrolled and accelerated multiplication of cells in the brain. If not treated early enough, it can lead to death. Despite multiple significant efforts and promising research outcomes, accurate segmentation and classification of tumors remain a challenge. The changes in tumor location, shape, and size make brain tumor identification extremely difficult. An Extreme Gradient Boosting (XGBoost) algorithm using is proposed in this work to classify four subtypes of brain tumor-normal, gliomas, meningiomas, and pituitary tumors. Because the dataset was limited in size, image augmentation using a conditional Generative Adversarial Network (cGAN) was used to expand the training data. Deep features, Two-Dimensional Fractional Fourier Transform (2D-FrFT) features, and geometric features are fused together to extract both global and local information from the Magnetic Resonance Imaging (MRI) scans. The model attained enhanced performance with a classification accuracy of 98.79% and sensitivity of 98.77% for the test images. In comparison to state-of-the-art algorithms employing the Kaggle brain tumor dataset, the suggested model showed a considerable improvement. The improved results show the prominence of feature fusion and establish XGBoost as an appropriate classifier for brain tumor detection in terms on testing accuracy, sensitivity and Area under receiver operating characteristic (AUROC) curve.