In this study, Rahman et al investigated the use of deep learning (DL) and machine learning (ML) algorithms to evaluate images of brain tumors created by magnetic resonance imaging (MRI) scans. Due to the high definition of MRI, which can result in textured tumor tissue that is difficult to discern, and other issues that include variability in the tumor’s morphology, brain tumor prognosis is often delayed. To address this, the researchers combined multiple feature extraction methods with ML and DL models to improve tumor recognition. Rahman et al trained and tested their models on publicly available MRI datasets (2,000 images in a smaller set and 7,023 images in a larger benchmark set) that included both tumor and non-tumor cases. Using the Local Binary Pattern (LBP) feature extraction method, Convolutional Neural Networks (CNN) demonstrated exceptional accuracy with 98.9% in recognizing the patterns and textures indicating a brain tumor, and Support Vector Classifier (SVC) performed recognition of the tumors with 96.7% accuracy. Even comparing the SVC algorithm within various feature spaces, it consistently had the highest rates of accuracy of over 90%. Despite the high levels of precision recognized in the methodologies, Rahman et al establish that both CNN and SVC are the most effective tools in accurately classifying brain tumors in MRI scans.