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Brain tumour detection is an important task in the medical field for early diagnosis and treatment. This project presents a novel system that uses eye movement analysis along with machine learning algorithms to predict the presence of brain tumours. The system first detects the eye using computer vision techniques and extracts features such as pupil size and blink rate, which are then analyzed using a trained model like Random Forest for prediction.
In addition to eye movement, the system also allows MRI scan images as input to improve accuracy and reliability. By combining both eye-based features and image-based analysis, the model provides better decision-making support. The integration of these two approaches helps in achieving faster and more efficient detection compared to traditional methods.
Overall, the proposed system is non-invasive, cost-effective, and easy to use. It can assist doctors in early-stage detection and can be useful in remote areas where advanced medical facilities are limited. This project demonstrates how machine learning and computer vision can be applied effectively in healthcare applications.
Keywords:
Brain Tumor Detection, Eye Movement Analysis, Machine Learning, Random Forest Algorithm, MRI Scan Images, Computer Vision, Pupil Detection, Blink Rate, Healthcare AI
Cite Article:
"Brain Tumour Detection, Eye Movement Analysis, Machine Learning, Random Forest, MRI, Computer Vision", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 4, page no.a141-a145, April-2026, Available :http://www.ijrti.org/papers/IJRTI2604019.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator