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@article{205173,
        author = {Tuwar Gayatri Sudhakar and Prof. Joshi V. M. and Shivam Gajanan Shendurkar and Payal Dattatray Harname},
        title = {AUTOMATED BRAIN TUMOR DETECTION SYSTEM USING DEEP LEARNING},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {5478-5485},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=205173},
        abstract = {Brain tumor is one of the most dangerous neurological diseases, and early detection is essential for proper treatment and patient survival. The proposed study, “Automated Brain Tumor Detection System Using Deep Learning,” aims to develop an intelligent system that can automatically detect brain tumors from MRI images using deep learning techniques, particularly Convolutional Neural Networks (CNN). Traditional methods of tumor diagnosis are time-consuming and depend heavily on radiologists’ expertise, whereas the proposed automated system provides faster and more accurate results. The methodology includes image acquisition, preprocessing, feature extraction, model training, and classification of MRI scans into tumor and non-tumor categories. The deep learning model learns complex image patterns and identifies abnormalities with high precision and reliability. Performance of the system is evaluated using parameters such as accuracy, precision, recall, and F1-score, and the experimental results show that the model achieves high accuracy in detecting brain tumors. The study highlights the importance of artificial intelligence in medical imaging and concludes that deep learning-based systems can significantly support healthcare professionals in early diagnosis, reduce human error, and improve treatment planning for brain tumor patients.},
        keywords = {— Brain Tumor Detection, Deep Learning, MRI Images, Convolutional Neural Network (CNN), Medical Image Processing},
        month = {June},
        }