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A platform for performing accurate medical risk analysis on patients using diagnostic data. Provides accurate classification of patients according to their cancer risk. Also accurately classifies Lung CT-Scan images into normal, benign and malignant classes.

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BronchoAnalytics

Medical Risk Analysis Platform

This platform is a comprehensive solution designed to enhance patient care by leveraging advanced data analysis.

Key Features:

  • Accurate Risk Assessment: Analyzes various diagnostic data points to identify potential health risks with high accuracy.
  • Cancer Risk Stratification: Classifies patients into specific risk groups for developing cancer, enabling targeted preventative measures.
  • Lung CT Scan Analysis: Provides automated and accurate classification of lung CT scans into normal, benign (non-cancerous), and malignant (cancerous) categories, supporting faster diagnosis. 🩻

Technical Infrastructure:

  • Development Stack: Python, Django (backend framework)
  • Machine Learning Capabilities: Tensorflow (deep learning framework) and various machine learning libraries
  • Data Management: Secure and scalable database utilizing SQLite3 with Django ORM (Object-Relational Mapper) wrapper
  • User Interface: User-friendly interface built with HTML, CSS, JavaScript, Bootstrap (front-end framework) for accessibility. ♿

Benefits:

This platform empowers healthcare professionals with data-driven insights to:

  • Improve early detection of potential health risks.
  • Implement personalized preventative strategies. 🩺
  • Enhance diagnostic accuracy and workflow efficiency. ✅ ⏱️

Overall, this platform represents a significant advancement in leveraging data analytics to improve patient outcomes and potentially save lives. ❤️


Landing Page 🚀


Cancer Risk Prediction 🔮


CT-Scan Classifier 🩻


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A platform for performing accurate medical risk analysis on patients using diagnostic data. Provides accurate classification of patients according to their cancer risk. Also accurately classifies Lung CT-Scan images into normal, benign and malignant classes.

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