Inspiration
Lung cancer is one of the leading causes of cancer-related deaths worldwide. Early detection can significantly improve survival rates, but manual diagnosis from CT scan images can be time-consuming and challenging. Our inspiration was to create an AI-powered system that can assist in the early detection of lung cancer quickly and efficiently.
What it does
The AI-Powered Lung Cancer Detection System analyzes lung CT scan images and predicts the possibility of lung cancer using machine learning/deep learning techniques. The system helps in identifying suspicious patterns and provides prediction results that can support medical analysis.
How we built it
We built the project using Python and machine learning libraries such as TensorFlow, Keras, NumPy, Pandas, and OpenCV.
- Collected and processed lung CT scan datasets
- Performed image preprocessing and normalization
- Trained a deep learning model for classification
- Developed a simple interface to upload scans and view predictions
- Tested the model with sample medical images
Challenges we ran into
- Finding high-quality and balanced medical datasets
- Improving model accuracy while avoiding overfitting
- Handling image preprocessing and large dataset sizes
- Limited computational resources for training deep learning models
- Making the prediction results understandable and user-friendly
Accomplishments that we're proud of
- Successfully developed a working prototype for lung cancer prediction
- Achieved promising prediction accuracy during testing
- Built an easy-to-use interface for image analysis
- Gained practical experience in AI for healthcare applications
- Completed the project within limited time and resources
What's next for AI-Powered Lung Cancer Detection System
- Improve model accuracy with larger datasets
- Add real-time prediction capabilities
- Integrate cloud storage and deployment
- Develop a mobile/web application version
- Collaborate with healthcare professionals for better validation
- Expand the system to detect multiple lung diseases in the future
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