Visual Q&A in Healthcare is a web-based platform that allows users to upload medical images, ask questions, and receive accurate answers using machine learning and computer vision techniques. The system leverages advanced algorithms and deep learning models trained on a large dataset of medical images and question-answer pairs.
Please note that the model files (pickle and joblib) are not included in this repository due to their large size. Uploading large binary files like these can be impractical for version control systems and may cause issues with repository size and download times.
If you need access to the model files, please contact us directly, and we'll be happy to provide you with the necessary files separately.
Welcome to Visual Q&A in Healthcare! Our platform utilizes advanced machine learning, deep learning, and computer vision techniques to provide accurate answers to medical queries based on uploaded images. With a user-friendly interface powered by Django framework, users can easily upload medical images, ask questions, and receive precise responses generated by our intelligent algorithms.
Our system incorporates machine learning models like Support Vector Machines and Random Forest, as well as deep learning models like ResNet50, to analyze and extract relevant information from the uploaded medical images. We leverage computer vision techniques, such as the Haar Cascade Frontal Face algorithm, to enhance the accuracy of our image analysis.
In addition to the question-answering functionality, our platform offers features like visual consultation with healthcare professionals, health tips for preventative care, and a blog section to provide valuable medical information and updates.
Our goal is to enhance healthcare services by leveraging cutting-edge technologies, providing accurate medical insights, and enabling users to make informed decisions about their health.
Explore our platform and experience the power of intelligent healthcare assistance!
- Image upload: Users can upload medical images such as X-rays or CT scans.
- Question submission: Users can submit questions related to the uploaded images.
- Image processing: Computer vision techniques are employed to process and analyze the uploaded medical images.
- Question analysis: Natural language processing algorithms analyze and understand the user's questions.
- Answer generation: The system generates accurate and relevant answers based on the processed images and analyzed questions.
- Model training and integration: Deep learning models are trained on a large dataset and integrated into the system for inference.
- User-friendly interface: The platform provides an intuitive and responsive user interface.
- Visual consultant integration: Users can seek expert advice and guidance through visual consultation with healthcare professionals.
- Additional features: The system offers health tips, precautions, causes of conditions, and a blog section for enhanced user experience.
- Programming Language: Python
- Computer Vision: OpenCV
- Machine Learning Algorithms: Support Vector Machine (SVM), Random Forest, Ranking Algorithm
- Deep Learning Framework: Caffe
- Pre-trained Model: ResNet50
- Frontend: HTML, CSS, JavaScript
- Backend: Django
- Database: Django Database
- Cloud Hosting and Storage: Microsoft Azure
- Clone the repository:
git clone https://github.com/Praveen-18/Cognitive_devops_VQA - Install the required dependencies:
pip install -r requirements.txt - Configure the database settings in
settings.py. - Run the server:
python manage.py runserver - Access the application in your web browser at
http://localhost:8000.
- Visit the homepage and create a user account.
- Upload a medical image and submit related questions.
- The system will process the image and generate accurate answers.
- Explore additional features such as virtual consultation, health tips, and the blog section.
Contributions to the project are welcome! If you have any ideas, bug reports, or feature requests, please open an issue or submit a pull request.
Special thanks to the following contributors for their valuable contributions to this project: