AI-Based Diagnostic System for Empyema Detection (Mobile + Web Application)
Abstract
The AI-Based Diagnostic System for Empyema Detection introduces an advanced, automated, and highly accurate approach to medical diagnosis using deep learning. The system focuses on detecting empyema—a severe pleural infection—by leveraging state-of-the-art medical imaging analysis powered by EfficientNetV2s. Through intelligent image processing and AI-driven prediction, this system supports doctors in making faster and more reliable clinical decisions.
This innovative solution integrates medical expertise with artificial intelligence, offering seamless data processing, intelligent decision-making, and enhanced diagnostic confidence. The system is designed to improve patient outcomes by ensuring early detection, reducing diagnostic delay, and providing reliable medical assistance even in resource-constrained environments.
1.1 Brief
This project aims to build a fully automated deep learning model for accurately detecting Empyema from chest X-ray images using advanced CNN architectures. The primary goal is to reduce diagnostic time, assist radiologists, and prevent late-stage complications by identifying subtle patterns that may not be easily visible through manual interpretation.
The system is deployed as both a mobile application (Flutter) and a web portal (MERN Stack), making it accessible, portable, and suitable for real-world clinical use. It provides a fast, reliable, and automated diagnostic tool that enhances medical decision-making.
1.3 Project Background
Empyema is a life-threatening pulmonary condition caused by pus buildup in the pleural cavity. It often arises from pneumonia, lung infections, or chest complications and can result in respiratory failure, sepsis, and even death if not diagnosed early. Traditional diagnostic methods require specialized equipment and expert radiologists, causing delays in treatment—especially in under-resourced areas.
Chest X-rays (CXR) are widely used for diagnosing thoracic diseases, but manual interpretation is time-consuming and prone to human error. Early signs of empyema are subtle and often overlooked. With rapid advancements in AI—especially deep learning—there is a growing opportunity to automate and enhance medical image analysis.
This project uses EfficientNetV2s, a cutting-edge convolutional neural network optimized for accuracy and performance. The model learns critical patterns from labeled chest X-ray datasets, identifying features indicative of empyema for fast and precise diagnosis. Its lightweight nature makes it ideal for mobile-based clinical deployment.
System Architecture & Technologies
- Mobile App (Flutter) – Android app for scanning and diagnosing X-rays
- Web App (MERN Stack) – Admin & doctor dashboard
- AI Model (EfficientNetV2s + TensorFlow) – Core diagnostic engine
- Google Colab – Model training and GPU-based optimization
- Firebase – Real-time database & user authentication
- DigitalOcean – Backend deployment for scalable performance
- Python – AI model scripting and backend integration
Proposed Solution
The proposed AI-powered diagnostic system addresses the challenges of conventional diagnosis by providing:
- Automated empyema detection from chest X-ray images
- Accurate, fast, and real-time predictions using EfficientNetV2s
- Mobile and web support for maximum flexibility
- Cloud-based storage, synchronization, and authentication
- A reliable tool for assisting radiologists and clinicians
- Early-stage detection to reduce mortality and improve treatment success
Training & Deployment
The deep learning model is trained using annotated medical datasets. Training and testing are carried out on Google Colab with GPU acceleration to ensure high accuracy and quick convergence. The backend logic, APIs, and model integration are developed in VS Code using Python, TensorFlow, and Node.js. Firebase ensures secure authentication and real-time synchronization of user and patient data. DigitalOcean is used for hosting backend services to ensure reliability and scalability in clinical environments.
System Features
- AI-based chest X-ray analysis
- Mobile app for instant diagnosis
- Doctor/Admin dashboard on web
- High accuracy EfficientNetV2s model
- Real-time prediction results
- Patient history & medical reports
- Firebase authentication & cloud storage
- Secure data handling and user access control
- Fast model inference optimized for mobile
- Interactive UI/UX for healthcare professionals
Conclusion
Deploying an AI-powered diagnostic system using EfficientNetV2s represents a major advancement in medical imaging and automated disease detection. By integrating mobile and web platforms with deep learning, the system enhances early detection, reduces diagnostic errors, and supports clinicians in making informed decisions—ultimately saving lives.
This project provides a scalable, accurate, and real-world ready diagnostic solution designed to transform empyema detection in both advanced and resource-limited healthcare environments.
