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@article{177527,
        author = {GANESH A and ABHINAV A and RUPESH V and SANDEEP G},
        title = {BRAIN TUMOUR DIAGNOSIS IN HUMANS USING MACHINE LEARNING},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {2715-2721},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=177527},
        abstract = {This paper proposes a smart and adaptive brain tumour detection system for real-time identification and classification of brain anomalies in medical imaging. Utilizing convolutional neural networks (CNNs) for visual diagnosis, the system classifies MRI brain scans as healthy, glioma tumour, meningioma tumour, pituitary tumour, or other abnormalities with 90% precision. Integrated with an Android application, the solution enables users to upload MRI images for instant analysis through a cloud-based inference model. The system performs image enhancement, segmentation, and multi-class tumour classification for accurate results. Coupled with advanced preprocessing techniques, the model is trained on a large dataset of labelled brain MRI images to ensure robust detection. The system supports early diagnosis strategies by providing instant feedback, improving treatment outcomes and minimizing health risks. Designed for real-world clinical environments, this solution offers an accessible, efficient, and automated approach to brain health monitoring and tumour management.},
        keywords = {Brain tumour detection, Convolutional Neural Network, MRI classification, Machine learning, Medical imaging, Healthcare automation.},
        month = {May},
        }