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Classification with Deep Learning \u0026 Gemini AI\n\n![Streamlit](https://img.shields.io/badge/Streamlit-Cloud-success?logo=streamlit)\n![TensorFlow](https://img.shields.io/badge/TensorFlow-2.x-orange?logo=tensorflow)\n![License](https://img.shields.io/badge/License-MIT-blue.svg)\n![Status](https://img.shields.io/badge/Status-Deployed-success)\n\n---\n\n## 📖 Overview\n\n**TumorScope** is a web application that classifies **brain MRI scans** into four categories:\n- **Glioma**\n- **Meningioma**\n- **Pituitary Tumor**\n- **No Tumor**\n\nThe app uses:\n- **Xception (Transfer Learning)**\n- **Custom CNN Model**\n- **Saliency Maps** for interpretability\n- **Gemini 1.5 Flash (Google AI)** to generate concise **medical explanations** for predictions\n\nThe project is deployed on **Streamlit Cloud** and provides an interactive user experience for radiologists, researchers, and students.\n\n---\n\n## ✨ Key Features\n\n- **Upload Brain MRI Images** for classification\n- **Dual-Model Selection**: Transfer Learning (Xception) or Custom CNN\n- **Saliency Map Generation** to visualize important image regions\n- **AI-Powered Explanations** via Gemini 1.5 Flash\n- **Interactive Probability Charts** with Plotly\n- **Secure Deployment** on Streamlit Cloud\n\n---\n\n## 🛠 Tech Stack\n\n| Component         | Technology              |\n|-------------------|-------------------------|\n| **Frontend**      | Streamlit               |\n| **Model Training**| TensorFlow / Keras      |\n| **Explainability**| Gemini 1.5 Flash        |\n| **Visualization** | OpenCV, Plotly          |\n| **Deployment**    | Streamlit Cloud         |\n\n\n---\n\n## 🚀 Getting Started\n\n### 1️⃣ Clone the Repo\n```bash\ngit clone https://github.com/rahatmoktadir03/tumor-scope.git\ncd tumor-scope\n```\n\n### 2️⃣ Install Dependencies\n```bash\npip install -r requirements.txt\n```\n\n### 3️⃣ Add Gemini API Key\n - Create a .env file in the project root:\n   ```init\n   GOOGLE_API_KEY=your-gemini-api-key\n   ```\n\n### 4️⃣ Run Locally\n```bash\nstreamlit run streamlit_app.py\n```\n\n---\n\n## 📊 Models and Accuracy\n- Xception (Transfer Learning) – ~99% accuracy\n- Custom CNN – ~98% accuracy\n- Saliency Maps highlight critical regions for model interpretability\n\n---\n\n## 🔮 Future Improvements\n- Chat with MRI scan (using multimodal LLMs)\n- Compare multiple models side-by-side\n- Generate detailed diagnostic reports for doctors\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frahatmoktadir03%2Ftumor-scope","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frahatmoktadir03%2Ftumor-scope","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frahatmoktadir03%2Ftumor-scope/lists"}