https://github.com/jcaperella29/dicom_processing_flask_app

🏥 DICOM Flask App – AI-Powered Lesion Detection A Flask-based web app for uploading, processing, and analyzing DICOM medical images. Uses DeepLesion (Faster R-CNN) for lesion detection and ResNet50 for classification. Features a multi-tab UI with sidebar navigation. A sample DICOM file is included for testing!
https://github.com/jcaperella29/dicom_processing_flask_app

ai-healthcare computer-vision dicom faster-rcnn flask healthcare-ai image-processing lesion-detection medical-imaging python pytorch radiology reset

Last synced: 3 months ago
JSON representation

🏥 DICOM Flask App – AI-Powered Lesion Detection A Flask-based web app for uploading, processing, and analyzing DICOM medical images. Uses DeepLesion (Faster R-CNN) for lesion detection and ResNet50 for classification. Features a multi-tab UI with sidebar navigation. A sample DICOM file is included for testing!

• Host: GitHub
• URL: https://github.com/jcaperella29/dicom_processing_flask_app
• Owner: jcaperella29
• Created: 2025-02-21T15:35:10.000Z (3 months ago)
• Default Branch: main
• Last Pushed: 2025-02-21T15:48:12.000Z (3 months ago)
• Last Synced: 2025-02-21T16:32:35.350Z (3 months ago)
• Topics: ai-healthcare, computer-vision, dicom, faster-rcnn, flask, healthcare-ai, image-processing, lesion-detection, medical-imaging, python, pytorch, radiology, reset
• Language: Python
• Homepage:
• Size: 0 Bytes
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
 DICOM Flask App – Multi-Tab Lesion Detector

📌 Overview

This Flask web app allows users to upload DICOM files, detect lesions using DeepLesion (Faster R-CNN), and classify them with ResNet50. The UI features a multi-tab layout with a sidebar for smooth navigation.

✅ Upload & Process DICOM Files

✅ Detect & Classify Lesions Automatically

✅ Multi-Tab UI with Sidebar Navigation

✅ Sample DICOM File Included for Testing

🛠 Installation & Setup

1️⃣ Clone the Repository

bash

Copy

Edit

git clone https://github.com/YOUR_GITHUB_USERNAME/DICOM-Flask-App.git

cd DICOM-Flask-App

2️⃣ Install Dependencies

bash

Copy

Edit

pip install -r requirements.txt

(If requirements.txt is missing, install manually:)

bash

Copy

Edit

pip install flask torch torchvision pydicom numpy matplotlib opencv-python

3️⃣ Run the Flask App

bash

Copy

Edit

python app.py

Then, open http://127.0.0.1:5000/ in your browser.

📂 Project Structure

graphql

Copy

Edit

DICOM-Flask-App/

│── static/                  # CSS & processed images

│   ├── style.css            # UI Styling (Velvet Room Theme)

│── templates/               # HTML Templates for Flask

│   ├── index.html           # Main UI (Tabs: Upload, Results)

│── uploads/                 # Stores uploaded DICOM files

│── sample.dcm               # Sample DICOM file for testing ✅

│── app.py                   # Flask Application

│── requirements.txt         # Dependencies

│── README.md                # This documentation

🚀 How to Use

1️⃣ Upload a DICOM File

Go to http://127.0.0.1:5000/

Click "Upload DICOM", select a file, and click "Upload & Process"

2️⃣ View Results

Click the "Results" tab to see detected lesions.

Lesions are shown with bounding boxes and classified as Tumor, Cyst, Hemorrhage, or Inflammation.

📝 Sample DICOM File

A sample DICOM file (se.dcm) is included in the repo for convenience.

If you don’t have a DICOM file, use this one for testing.

🔥 Future Upgrades

🔹 Grad-CAM Heatmaps – Highlight lesion focus areas.

🔹 DICOM Export – Save processed images back into DICOM format.

🔹 Automatic Report Generation – AI-generated text reports for findings.