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The UI features a multi-tab layout with a sidebar for smooth navigation.\r\n\r\n✅ Upload \u0026 Process DICOM Files\r\n✅ Detect \u0026 Classify Lesions Automatically\r\n✅ Multi-Tab UI with Sidebar Navigation\r\n✅ Sample DICOM File Included for Testing\r\n\r\n🛠 Installation \u0026 Setup\r\n1️⃣ Clone the Repository\r\nbash\r\nCopy\r\nEdit\r\ngit clone https://github.com/YOUR_GITHUB_USERNAME/DICOM-Flask-App.git\r\ncd DICOM-Flask-App\r\n2️⃣ Install Dependencies\r\nbash\r\nCopy\r\nEdit\r\npip install -r requirements.txt\r\n(If requirements.txt is missing, install manually:)\r\n\r\nbash\r\nCopy\r\nEdit\r\npip install flask torch torchvision pydicom numpy matplotlib opencv-python\r\n3️⃣ Run the Flask App\r\nbash\r\nCopy\r\nEdit\r\npython app.py\r\nThen, open http://127.0.0.1:5000/ in your browser.\r\n\r\n📂 Project Structure\r\ngraphql\r\nCopy\r\nEdit\r\nDICOM-Flask-App/\r\n│── static/                  # CSS \u0026 processed images\r\n│   ├── style.css            # UI Styling (Velvet Room Theme)\r\n│── templates/               # HTML Templates for Flask\r\n│   ├── index.html           # Main UI (Tabs: Upload, Results)\r\n│── uploads/                 # Stores uploaded DICOM files\r\n│── sample.dcm               # Sample DICOM file for testing ✅\r\n│── app.py                   # Flask Application\r\n│── requirements.txt         # Dependencies\r\n│── README.md                # This documentation\r\n🚀 How to Use\r\n1️⃣ Upload a DICOM File\r\nGo to http://127.0.0.1:5000/\r\nClick \"Upload DICOM\", select a file, and click \"Upload \u0026 Process\"\r\n2️⃣ View Results\r\nClick the \"Results\" tab to see detected lesions.\r\nLesions are shown with bounding boxes and classified as Tumor, Cyst, Hemorrhage, or Inflammation.\r\n📝 Sample DICOM File\r\nA sample DICOM file (se.dcm) is included in the repo for convenience.\r\n\r\nIf you don’t have a DICOM file, use this one for testing.\r\n🔥 Future Upgrades\r\n🔹 Grad-CAM Heatmaps – Highlight lesion focus areas.\r\n🔹 DICOM Export – Save processed images back into DICOM format.\r\n🔹 Automatic Report Generation – AI-generated text reports for findings.\r\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjcaperella29%2Fdicom_processing_flask_app","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjcaperella29%2Fdicom_processing_flask_app","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjcaperella29%2Fdicom_processing_flask_app/lists"}