A Review on Multi-Class Brain Tumor Detection and Classification: Trends and Future Directions

Abstract

The brain tumor classification is crucial to timely detecting patients and planning effective treatment. Multi-class brain tumor analysis does not merely recognize the presence of a tumor but tries to separate among various forms of tumors, including glioma, meningioma, and pituitary adenoma. The subjective interpretation of MRIs by hand has been known to be subjective, tedious and prone to human error. Modern advancements in deep learning (DL) and machine learning (ML) have improved the precision and reliability of the automation of diagnosis of brain tumors significantly. Traditional ML models such as SVM, KNN and Random Forest make use of hand crafted features, whereas modern DL models, specifically Convolutional Neural Networks (CNNs) can extract high level spatial features in medical images automatically and thus perform better in classification. Recent developments like transfer learning, Vision Transformers (ViT), Explainable AI (XAI) and Federated Learning are moving towards more interpretable, privacy protecting and generalizable diagnostic models. This work is a comprehensive look at imaging methods, classification, current trends, challenges, and future trends in the analysis of multi-class brain tumors. Explainable frameworks, lightweight models, and large, multi institutional datasets are required for real-time clinical deployment, according to the research, in order to improve the diagnostic procedures' accuracy and dependability.