A systematic review of trending technologies in non-invasive automatic brain tumor detection

This manuscript provides a detailed review of state-of-the-art techniques for identifying brain tumors using magnetic resonance imaging (MRI) analysis, focusing on automatic brain tumor detection (ABTD). The study is driven by the critical need to improve the accuracy and effectiveness of tumor identification in clinical settings, where traditional methods can be prone to manual error and extensive. The review covers five key stages: data collection, preprocessing, segmentation, feature extraction, and classification. Techniques are systematically categorized and evaluated based on features, performance metrics, and clinical applicability. A structured framework is proposed for assessing ABTD methods, offering a comprehensive comparison of pros, cons, processing speed, and detection accuracy. The review highlights the effectiveness of various techniques, with the combination of LuNet and Deep Convolutional Neural Networks (DCNN) obtained 99.7% accuracy. Areas for improvement in current methodologies are identified, and future research directions are suggested, supported by quantitative analysis and statistical evaluation. This work aims to advance the development of more reliable and practical ABTD techniques for use in real-world clinical environments.