Lungs Cancer Detection
Welcome to our Lung Cancer Detection project! This README file will guide you through the process of setting up and utilizing our application for the early detection of lung cancer using supervised learning techniques.
Introduction Lung cancer is one of the leading causes of cancer-related deaths worldwide. Early detection plays a crucial role in improving the survival rate of patients. Our project aims to assist in the early detection of lung cancer through the use of machine learning algorithms.
Features Supervised Learning: We employ supervised learning techniques to train our model on labeled datasets for accurate lung cancer detection. Web Application: Our application is built using Django framework, providing an intuitive user interface for seamless interaction. Libraries: We utilize TensorFlow and Keras libraries for building and training deep learning models. Installation To set up the environment and run the application, follow these steps:
Clone the repository:
bash Copy code git clone cd lung-cancer-detection Install dependencies:
bash Copy code pip install -r requirements.txt Run migrations:
bash Copy code python manage.py migrate Start the development server:
bash Copy code python manage.py runserver Access the application at http://localhost:8000 in your web browser.
Usage Upload Image: Users can upload chest X-ray images for analysis. Analyze Image: Our model will process the uploaded image and provide the likelihood of lung cancer. View Results: Results will be displayed indicating whether cancer is detected and the confidence level. Dataset Our model has been trained on a curated dataset of chest X-ray images with corresponding labels indicating the presence or absence of lung cancer. The dataset has been preprocessed for optimal model training.
Model Architecture We have utilized a deep learning architecture implemented using TensorFlow and Keras for the detection of lung cancer. The model has been trained on the dataset to learn patterns indicative of lung cancer presence.
Future Enhancements Integration of additional features for more comprehensive analysis. Deployment on cloud platforms for wider accessibility. Continuous improvement of the model through ongoing research and development. Contributions Contributions to the project are welcome! Whether it's bug fixes, feature enhancements, or documentation improvements, feel free to open a pull request.
Support
If you encounter any issues or have any questions, please feel free to contact us at sandeshpol268@gmail.com
Thank you for using our Lung Cancer Detection application. Together, we can make a difference in early cancer detection and ultimately save lives.
