DETECTING BRAIN TUMOR USING
Divyanshu Raj ,Shivanjal Srivastava,Abhijeet Badhani,Dilpreet Kaur , Kumar Rethik
Abstract
Brain tumor detection is a critical task in the medical field, as early diagnosis plays a vital role in patient survival and treatment effectiveness. Traditional diagnostic methods involving manual analysis of MRI scans are often time-consuming and prone to human error. This project proposes an AI-powered system developed using Python, deep learning, and image processing techniques to detect brain tumors from MRI images with high accuracy. The system integrates a Flutter-based frontend that allows users to upload medical scans, while the backend processes the images using a Convolutional Neural Network (CNN) model built with TensorFlow and Keras. The model classifies the MRI images as tumor or non-tumor, and the results are displayed to the user in real time. The system aims to support medical professionals by providing faster and more accurate diagnosis tools, especially in areas with limited access to expert radiologists. Cloud storage and backend services ensure scalability and secure data handling, offering a reliable and user-friendly platform for brain tumor detection.
References
1. Kaggle. (n.d.). Brain Tumor MRI Images Dataset. Retrieved from https://www.kaggle.com 2. Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A., Ciompi, F., Ghafoorian, M., ... & Sánchez, C. I. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis, 42, 60–88. 3. Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention (pp. 234–241). Springer, Cham. 4. Chollet, F. (2017). Deep Learning with Python. Manning Publications. 5. Abadi, M., et al. (2016). TensorFlow: Large-scale machine learning on heterogeneous distributed systems. arXiv preprint arXiv:1603.04467. Retrieved from https:// www.tensorflow.org 6. Flask Documentation. (n.d.). Flask: Python Web Framework. Retrieved from https://flask.palletsprojects.com 7. Flutter Documentation. (n.d.). Build Beautiful Apps. Retrieved from https://flutter.dev 8. Brownlee, J. (2018). Deep Learning for Computer Vision: Image Classification, Object Detection, and Face Recognition in Python. Machine Learning Mastery. 9. Badrinarayanan, V., Kendall, A., & Cipolla, R. (2017). SegNet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(12), 2481–2495. 10. Radiopaedia. (n.d.). MRI Brain Tumor Imaging Guide. Retrieved from https://radiopaedia.org