This project leverages advanced deep learning techniques to assist in the diagnosis of brain tumors using MRI scans. It aims to classify tumors into four categories: glioma, meningioma, pituitary tumors, or no tumor, providing an interactive interface for users, including patients and medical professionals.
- Developed using Python, TensorFlow, and Keras for building and training the deep learning models.
- Built with Streamlit, providing a user-friendly, interactive interface for uploading MRI scans and generating predictions.
- A pre-trained Xception model with transfer learning for high accuracy.
- A custom-built CNN model for comparative insights.
- Powered by Plotly for detailed bar charts of class probabilities.
- Utilized OpenCV for generating saliency maps to highlight critical areas of the MRI scan.
- Incorporated Gemini, Llama, and Gemma2 for generating textual explanations and a comprehensive report.
- The system classifies uploaded MRI scans and provides a detailed probability distribution for the predicted tumor type.
- Generates visual overlays highlighting the critical regions the model focuses on during classification.
- Aims to aid medical professionals in better understanding the results.
- Answers are dynamically generated by multimodal LLMs, combining text and image analysis.
- Provides a detailed report including:
- Model predictions.
- Additional insights.
- Actionable next steps for patients and doctors.