Brain Tumor Classification
NeuralScan is an advanced deep learning model designed to classify brain tumors from MRI scans. It employs a convolutional neural network architecture with attention mechanisms for accurate feature extraction. Augmentation techniques and transfer learning enhance its performance. ...learn more
Project status: Concept
Overview / Usage
Challenge of Brain Tumor Detection:
Addressing the intricate challenge of precisely identifying brain tumors demands innovative solutions beyond conventional methods.
Importance of Early Detection:
Timely detection is paramount, significantly impacting patient prognosis and influencing the success of treatment strategies.
Current Limitations:
Existing diagnostic methods may fall short in capturing tumors' early stages and can occasionally lead to misinterpretation of medical images.
Empowering Medical Professionals:
We aim to equip medical practitioners with a robust tool that enhances their diagnostic prowess, enabling informed decisions based on accurate results.
Bridge to Enhanced Care:
Our solution serves as a bridge between pioneering technology and patient care, refining brain tumor diagnosis accuracy and refining treatment planning.
Methodology / Approach
- Data Collection: Gather labeled brain MRI images, including both tumor and non-tumor cases.
- Image Processing: Enhance image quality, remove noise, and standardize orientations.
- ROI Extraction: Use segmentation to focus on the brain region of interest.
- Feature Extraction: Extract relevant features like texture and shape from the ROI.
- Machine Learning: Train a model using features and labels, using algorithms like CNNs or SVMs.
- Cross-Validation: Validate model performance using separate data and metrics like accuracy and F1-score.
- Hyperparameter Tuning: Optimize model parameters for better performance.
- Validation: Test the final model on new data not seen during training.
- Deployment: Integrate the model into a user-friendly application for medical professionals.
- Ethical Considerations: Adhere to data privacy regulations and ensure ethical handling of patient data.
Technologies Used
DAL (Data Analytics Library)
Leveraging this library, we enhance data analysis capabilities, facilitating more insightful and accurate tumor detection.
DNN (Deep Neural Networks)
Utilizing deep learning techniques from DNN, our project employs neural networks for robust and advanced image analysis.
Tensorflow
Incorporating TensorFlow, a widely-used deep learning framework, we optimize machine learning models for accurate brain tumor classification.
Documents and Presentations
Repository
https://github.com/RajarshiBarman/Brain_Tumor_Classification.git
Collaborators
There are no people to show.