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@article{205605,
        author = {Naveena M and K Padmavathi},
        title = {A Survey on Machine Learning and Deep Learning Techniques for Brain Tumor Detection in MRI Images},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {6998-7004},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=205605},
        abstract = {Malignant tumor detection using human image evaluation which are bound to human errors and time consuming. MRI (Magnetic Resonance Imaging) image tool has become an important tool to locate the brain tumors as brain tumors can be detected in various stages of development which vary in size and can be located in any part of the brain. Detecting Brain-Tumor using MRI is a crucial task in medical diagnosis, where early and accurate identification helps the patients. Over the past decade, machine learning and deep learning techniques have been widely adopted for automated tumor detection, segmentation, and classification. This paper provides us with a literary survey of existing approaches, including preprocessing techniques, segmentation methods, feature extraction strategies, optimization algorithms, and classification models.
The survey highlights the strengths and weaknesses of traditional machine learning models, deep learning architectures, and hybrid frameworks. Special attention is given on feature fusion techniques and optimization strategies that enhance model performance. In spite of achieving high accuracy, current approaches face limitations such as static feature fusion, lack of adaptability, and absence of uncertainty modeling. This paper identifies key research gaps and outlines future directions toward developing adaptive, robust, and intelligent brain tumor detection systems.},
        keywords = {Brain Tumour Detection, Convolutional Neural Network, Deep Learning, Machine Learning, MRI},
        month = {June},
        }