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@article{180491,
        author = {Sonia and Dr. Asha Rani},
        title = {MACHINE AND DEEP LEARNING APPROACHES FOR BRAIN TUMOR SEVERITY CLASSIFICATION: A COMPREHENSIVE REVIEW},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {6155-6162},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=180491},
        abstract = {Brain tumor classification based on severity levels—such as benign, low-grade, or high-grade malignancies—is a critical component of medical diagnosis and treatment planning. In recent years, machine learning (ML) and deep learning (DL) techniques have demonstrated remarkable potential in automating the detection, segmentation, and grading of brain tumors using radiological imaging modalities like MRI and CT scans. This comprehensive review explores the landscape of ML/DL-based approaches for brain tumor severity classification, emphasizing model architectures, feature extraction methods, data preprocessing strategies, and clinical datasets. The study analyses traditional ML algorithms like SVM and Random Forest alongside state-of-the-art deep learning frameworks such as CNNs, U-Nets, and hybrid attention-based models. Performance evaluation across different studies is discussed with respect to accuracy, precision, F1-score, and interpretability. Key challenges identified include data imbalance, annotation scarcity, generalizability across diverse imaging centres, and real-time clinical deployment. Finally, the paper outlines promising research directions involving explainable AI, multimodal data fusion, and federated learning to enhance the robustness and trustworthiness of AI-assisted tumor grading systems.},
        keywords = {Brain tumour Detection, Machine learning, Deep Learning, Accuracy.},
        month = {July},
        }