HistoAI: Intelligent Early Cancer Detection 🧬

HistoAI takes the uncertainty out of early cancer detection with intelligent, data-driven precision. Designed to support doctors and empower patients, it leverages cutting-edge machine learning to analyze diagnostic data, spot subtle patterns, and predict the risk of breast cancer — faster and more accurately than ever before.

From real-time analysis to personalized risk assessments, HistoAI bridges the gap between technology and healthcare, delivering results that matter when time is critical. It’s smart, seamless, and built for a future where AI and medicine work hand in hand to save lives.

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🚀 Features

• 🧠 Machine learning-powered breast cancer risk prediction
• ⚡ Real-time diagnostic data analysis
• 🌐 Frontend developed with React
• 🔥 Backend powered by Flask (Python)
• 📦 Dockerized frontend and backend for easy deployment
• 🔄 Fully automated CI/CD pipeline using Jenkins
• 🧪 Automated end-to-end testing integrated into the pipeline
• 🚀 Scalable and production-ready architecture

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🛠️ Development Preparation

🔧 Frontend & Backend

• Built frontend using React.js
• Developed backend using Flask, integrating ML models (ResNet50)

🐳 Dockerization

• Created Dockerfiles for both frontend and backend
• Containerized apps for consistent environment setup
• Used Docker Compose for orchestration

🔁 Version Control

• Managed via Git
• All codebases (frontend, backend, ML, CI/CD) tracked in one repo

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⚙️ CI/CD Pipeline with Jenkins

🧱 Jenkins Setup

• Configured Jenkins for automated Continuous Integration
• Pipeline triggers on git push

🛠 Docker Build and Deployment

• Built optimized, multi-stage Docker images
• Used Docker Compose to map:
  • Frontend: http://localhost
  • Backend API: http://localhost:5000

🚀 Pipeline Optimization

• Leveraged build caching and efficient sourcing
• Optimized build workflow for faster deployments

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🧪 Deployment and Testing

🖥 Local Deployment

git clone https://github.com/keerthana777z/histoai.git
cd histoai
docker-compose up --build

• Access Frontend: http://localhost
• Access Backend API: http://localhost:5000

✅ Automated End-to-End Testing

• Test scripts run automatically via Jenkins after deployment
• Verified full-stack functionality and communication

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📈 Outcome and Impact

• ✅ Completed Deployment: Full-stack app running locally
• ⚡ Boosted Efficiency: Fast CI/CD cycle with Jenkins
• 🌍 Scalable Design: Ready for real-world cloud deployment

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🧰 Technologies Used

• Frontend: React.js
• Backend: Flask (Python)
• ML Model: ResNet50
• Containerization: Docker, Docker Compose
• CI/CD: Jenkins
• Version Control: Git

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🌱 Future Enhancements

• ☁️ Cloud Deployment (AWS / GCP / Azure)
• 📊 Advanced monitoring & logging (Prometheus, Grafana)
• 🏥 Healthcare DB integration for real-world validation
• 🔍 Model explainability using SHAP or LIME

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👩‍💻 Author

AR Keerthana 🔗 GitHub Profile