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--- |
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title: 'CerebAI: AI-Powered Stroke Detection System' |
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emoji: 🧠 |
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colorFrom: red |
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colorTo: indigo |
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sdk: streamlit |
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app_file: cerebAI.py |
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license: mit |
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sdk_version: 1.50.0 |
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--- |
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# CerebAI: AI-Powered Stroke Detection System |
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## Project Overview |
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CerebAI is a deep learning application designed to assist medical professionals by rapidly classifying CT scan images for the presence and type of stroke. Built on the advanced ConvNeXt architecture, the system provides a robust diagnosis coupled with a critical eXplainable AI (XAI) feature, ensuring predictions are transparent and medically intuitive. |
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This project showcases high-performance multiclass classification and deployment readiness. |
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## Key Technical Achievements |
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| Metric | Score (Test Set) | Implication | |
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| :--- | :--- | :--- | |
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| Mean IoU (mIoU) | ~0.9843 | Top-tier performance for pixel-level prediction quality. | |
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| Test F1 Score (Weighted) | ~0.9805 | Excellent balance between Precision and Recall across all three classes. | |
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| Model Architecture | ConvNeXt Base | State-of-the-art model designed for robust feature extraction from medical images. | |
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## Interpretability (XAI Feature) |
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The system uses **Integrated Gradients (IG)** from the Captum library to generate a heatmap overlay. |
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* **Function:** IG highlights the specific pixels that most strongly influence the model's final diagnosis. |
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* **Clinical Value:** This visual evidence helps doctors verify the prediction by confirming the model is focusing on the actual pathology (the stroke region) and not on noise or artifacts. |
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## Deployment and Setup |
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### Local Run Instructions |
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1. **Clone the Repository:** |
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```bash |
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git clone [https://github.com/ar-shenoy/cerebAI.git](https://github.com/ar-shenoy/cerebAI.git) |
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cd cerebai_streamlit |
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``` |
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2. **Activate Environment:** Ensure your virtual environment is active. |
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```bash |
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.\venv\Scripts\activate |
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``` |
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3. **Install Dependencies:** |
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```bash |
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pip install -r requirements.txt |
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``` |
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4. **Place Model Weights:** Ensure your trained model file (best_model.pth) is in the project root directory. |
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5. **Launch App:** |
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```bash |
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streamlit run cerebAI.py |
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``` |
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### Streamlit Deployment (Cloud) |
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This repository is configured for one-click deployment on the Streamlit Community Cloud. The app is optimized to run on a shared CPU by limiting the Integrated Gradients calculation to 20 steps to ensure fast performance. |
