A brain tumor is the result of abnormal growth of cells in the central nervous system (CNS), widely considered as a complex and diverse clinical entity that is difficult to diagnose and cure. In this study, we focus on current advances in medical imaging, particularly magnetic resonance imaging (MRI), and how machine learning (ML) and deep learning (DL) algorithms might be combined with clinical assessments to improve brain tumor diagnosis. Due to its superior contrast resolution and safety compared to other imaging methods, MRI is highlighted as the preferred imaging modality for brain tumors. The challenges related to brain tumor analysis in different processes including detection, segmentation, classification, and survival prediction are addressed along with how ML/DL approaches significantly improve these steps. We systematically analyzed 107 studies (2018–2024) employing ML, DL, and hybrid models across publicly available datasets such as BraTS, TCIA, and Figshare. In the light of recent developments in brain tumor analysis, many algorithms have been proposed to accurately obtain ontological characteristics of tumors, enhancing diagnostic precision and personalized therapeutic strategies.

Detecting moving objects in videos is an evolving area of research, with important implications in many computer vision applications. In this paper, we propose a new detection approach by combining background subtraction and multi-level image thresholding based on fuzzy entropy, powered by the differential evolution (DE) algorithm. The first step of our method is background subtraction, aiming to isolate moving objects by eliminating the static background. However, this approach can be sensitive to lighting variations and background changes, thus limiting its accuracy. To overcome these limitations, we introduce multi-level image thresholding based on fuzzy entropy. This method exploits the intrinsic variability of moving objects rather than simply differentiating against the background. By adjusting thresholds locally, our approach better adapts to changing environmental conditions. The key element of our proposal lies in the optimization of the fuzzy entropy threshold parameters using the differential evolution algorithm. We chose DE for its robustness and efficiency in handling continuous optimization problems, which makes it well-suited for complex tasks like multi-level image thresholding. By iteratively adjusting the thresholds, we maximize the detection of moving objects while minimizing false positives, thereby improving the robustness and accuracy of the method. Our experiments on test video sequences demonstrate the effectiveness of our approach, highlighting a significant improvement in moving object detection compared to traditional methods. This promising methodology paves the way for future advances in moving object detection, with potential applications in surveillance, robotics, and computer vision in general.