Overview

NeumDetect is a cutting-edge medical imaging application that leverages deep learning and computer vision technologies to detect pneumonia from chest X-ray images. This project demonstrates the practical application of AI in healthcare, focusing on early and accurate diagnosis to improve patient outcomes.

🛠️ Technical Stack

• AI & Machine Learning

  • TensorFlow/Keras for deep learning implementation
  • Convolutional Neural Networks (CNN) for image processing
  • Transfer Learning with pre-trained models
  • Data augmentation techniques for improved accuracy

• Architecture & Model Design

  • Deep Learning architecture with CNN layers
  • Ensemble modeling for enhanced prediction accuracy
  • Custom model architecture for medical imaging
  • Integration with ChatGPT API for result interpretation

• Data Processing & Analysis

  • Advanced image preprocessing pipelines
  • Robust data augmentation strategies
  • Kaggle medical imaging datasets
  • Custom validation and testing frameworks

⭐ Key Features

• Real-time pneumonia detection from X-ray images
• High accuracy in medical diagnosis
• Interpretable AI results with explanation
• User-friendly medical interface
• Rapid processing and analysis
• Comprehensive diagnostic reports
• Support for various X-ray image formats
• Integration with medical workflows

🏗️ Architecture Overview

The project implements a sophisticated deep learning architecture:

• Input Layer: Processes chest X-ray images
• Feature Extraction: Multiple CNN layers with pooling
• Classification Layer: Binary classification (Normal/Pneumonia)
• Output Layer: Probability-based diagnosis

📱 UI Showcase - Streamlit Interface

🎯 Training Dataset Examples

Normal Chest X-rays

Clear lung fields without infiltrates

Pneumonia X-rays

Visible infiltrates indicating pneumonia

Healthy chest structure

Bilateral pneumonia patterns

🔍 Technical Highlights

• Implementation of transfer learning techniques
• Custom data augmentation pipeline
• Integration of medical imaging best practices
• Robust validation methodology
• Performance optimization for medical environments
• Enhanced model interpretability

📊 Model Performance

• Training accuracy: >94%
• Validation accuracy: >92%
• Test set performance: >90%
• Fast inference time: <2 seconds per image

📚 Key Learnings

• Deep learning application in medical imaging
• Medical data preprocessing techniques
• Model optimization for healthcare applications
• Implementation of AI ethics in healthcare
• Integration of ML systems in medical workflows

🏥 Healthcare Impact

• Early detection of pneumonia cases
• Reduced diagnostic time
• Support for medical professionals
• Improved patient outcomes
• Accessible medical AI technology

🛠️ Development Practices

• Strict medical data handling protocols
• Comprehensive validation processes
• Regular performance benchmarking
• Continuous model improvement
• Documentation of medical applications