markdown# 🫁 Pneumonia Detection from Chest X-Rays

An AI-powered deep learning application for detecting pneumonia from chest X-ray images using Transfer Learning with MobileNetV2. Achieves 94% accuracy with interpretable Grad-CAM visualizations.

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📋 Table of Contents

• Overview
• Key Features
• Model Performance
• Architecture
• Installation
• Usage
• Project Structure
• Dataset
• Model Training
• Results & Visualizations
• Technologies Used
• Disclaimer
• Future Improvements
• Contributing
• License
• Contact
• Acknowledgments

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🎯 Overview

This project addresses the critical healthcare challenge of pneumonia diagnosis through automated chest X-ray analysis. Using state-of-the-art deep learning techniques, the system can classify chest X-rays as Normal or Pneumonia with 94% accuracy, provide visual explanations of model predictions using Grad-CAM, and offer an intuitive web interface for healthcare professionals and researchers.

Use Cases: Screening tool for healthcare facilities | Educational resource for medical students | Research platform for ML practitioners | Prototype for clinical decision support systems

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✨ Key Features

🤖 High-Accuracy Model

• 94% test accuracy using MobileNetV2 transfer learning
• Robust performance on imbalanced datasets
• Fast inference (~1 second per image)

🔬 Interpretability

• Grad-CAM heatmap visualization
• Shows which lung regions influenced the prediction
• Builds trust and transparency in AI decisions

💻 User-Friendly Interface

• Interactive Streamlit web application
• Drag-and-drop image upload
• Real-time prediction and visualization
• Mobile-responsive design

📊 Comprehensive Analysis

• Prediction confidence scores
• Class probability breakdown
• Visual and numerical results

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📊 Model Performance

Test Set Metrics

Metric	Score
Accuracy	94.23%
Precision	94.56%
Recall	94.12%
F1-Score	94.34%
AUC-ROC	0.968

Confusion Matrix

                 Predicted
               Normal  Pneumonia
Actual Normal    220      14
      Pneumonia   22     368

Key Insights

• Low False Negative Rate: Critical for medical applications
• High Specificity: 94% - Minimizes unnecessary treatments
• High Sensitivity: 94% - Catches most pneumonia cases
• Balanced Performance: Works well on both classes despite data imbalance

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🏗️ Architecture

Model Overview

Input (224×224×3)
    ↓
MobileNetV2 Base (Pre-trained on ImageNet)
    ↓
Global Average Pooling
    ↓
Dropout (0.5)
    ↓
Dense (128 units, ReLU)
    ↓
Dropout (0.5)
    ↓
Dense (1 unit, Sigmoid)
    ↓
Output (Probability)

Training Strategy

Phase 1: Feature Extraction

• MobileNetV2 base frozen
• Train only top layers
• Learning rate: 0.001
• Epochs: 10

Phase 2: Fine-Tuning

• Unfreeze last 50 layers
• Lower learning rate: 5e-6
• Epochs: 15
• Total parameters: 4.3M

Key Techniques

✅ Transfer Learning (ImageNet → Medical Images) | ✅ Data Augmentation (rotation, zoom, shift, flip) | ✅ Class Weighting (handle 3:1 imbalance) | ✅ Early Stopping & LR Reduction | ✅ Batch Normalization | ✅ Dropout Regularization

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🚀 Installation

Prerequisites

• Python 3.8 or higher
• pip package manager
• 8GB RAM minimum
• (Optional) NVIDIA GPU with CUDA for faster training

Step 1: Clone the Repository

git clone https://github.com/yourusername/pneumonia-detection.git
cd pneumonia-detection

Step 2: Create Virtual Environment (Recommended)

# Windows
python -m venv venv
venv\Scripts\activate

# Mac/Linux
python3 -m venv venv
source venv/bin/activate

Step 3: Install Dependencies

pip install -r requirements.txt

Step 4: Download Pre-trained Model

Download mobilenet_best.h5 from releases and place in models/ folder.

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💻 Usage

Web Application (Streamlit)

Start the app:

streamlit run app.py

Access in browser: http://localhost:8501

How to use:

1. Upload a chest X-ray image (JPEG/PNG)
2. Click "Analyze Image"
3. View prediction, confidence, and heatmap

Python API (For Integration)

import tensorflow as tf
from tensorflow.keras.preprocessing import image
import numpy as np

# Load model
model = tf.keras.models.load_model('models/mobilenet_best.h5')

# Load and preprocess image
img = image.load_img('path/to/xray.jpg', target_size=(224, 224))
img_array = image.img_to_array(img) / 255.0
img_array = np.expand_dims(img_array, axis=0)

# Predict
prediction = model.predict(img_array)[0][0]

if prediction > 0.5:
    print(f"PNEUMONIA detected (confidence: {prediction*100:.1f}%)")
else:
    print(f"NORMAL (confidence: {(1-prediction)*100:.1f}%)")

Training from Scratch

# Navigate to notebooks
cd notebooks

# Run training notebooks in order
jupyter notebook 01_eda.ipynb
jupyter notebook 02_baseline_model.ipynb
jupyter notebook 03_transfer_learning.ipynb

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📁 Project Structure

Medical Image Classification/
│
├── 📁 data/
│   └── chest_xray/
│       ├── train/           # 5,216 training images
│       ├── val/             # 16 validation images
│       └── test/            # 624 test images
│
├── 📁 models/
│   ├── baseline_cnn_best.h5        # 90% accuracy baseline
│   └── mobilenet_best.h5           # 94% accuracy final model
│
├── 📁 results/
│   ├── plots/
│   │   ├── gradcam/                # Grad-CAM visualizations
│   │   ├── confusion_matrix.png
│   │   ├── roc_curve.png
│   │   ├── training_history.png
│   │   └── model_comparison.png
│   └── metrics/
│       ├── mobilenet_results.json
│       └── mobilenet_history.csv
│
├── 📁 notebooks/
│   ├── 01_eda.ipynb                # Exploratory Data Analysis
│   ├── 02_baseline_model.ipynb     # Custom CNN training
│   └── 03_transfer_learning.ipynb  # MobileNetV2 training
│
├── 📄 app.py                        # Streamlit web application
├── 📄 requirements.txt              # Python dependencies
├── 📄 README.md                     # This file
└── 📄 LICENSE                       # MIT License

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📊 Dataset

Source

• Kaggle: Chest X-Ray Pneumonia Dataset
• Original Paper: Kermany et al. (2018) - "Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning"

Statistics

Split	Normal	Pneumonia	Total
Train	1,341	3,875	5,216
Val	8	8	16
Test	234	390	624
Total	1,583	4,273	5,863

Characteristics

• Image Format: JPEG
• Resolution: Variable (typically 1024×1024 to 2048×2048)
• Color: Grayscale (converted to RGB for model)
• Source: Pediatric patients (1-5 years old)
• Types of Pneumonia: Bacterial and Viral

Data Preprocessing

1. Resize to 224×224 pixels
2. Normalize pixel values (0-1 range)
3. Convert grayscale to RGB (channel duplication)
4. Apply data augmentation (training only)

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🎓 Model Training

Baseline CNN (Week 2)

Architecture: 4 convolutional blocks | Custom CNN from scratch | 150×150 input size

Results: Accuracy: 90% | Parameters: ~2M | Training time: ~30 minutes

Transfer Learning (Week 3)

Why MobileNetV2? ✅ Lightweight (4.3M parameters) | ✅ Fast inference | ✅ Excellent for mobile/web deployment | ✅ Strong ImageNet features transfer well to medical images

Training Details:

• Optimizer: Adam
• Loss Function: Binary Crossentropy
• Batch Size: 32
• Total Epochs: 25 (Phase 1: 10, Phase 2: 15)
• Data Augmentation: Rotation (±15°), Zoom (±10%), Shift (±10%), Flip
• Class Weights: {Normal: 0.78, Pneumonia: 1.35}
• Hardware: NVIDIA GPU / CPU
• Training Time: ~2 hours

Hyperparameters:

CONFIG = {
    'img_size': (224, 224),
    'batch_size': 32,
    'initial_lr': 0.001,
    'fine_tune_lr': 5e-6,
    'dropout': 0.5,
    'initial_epochs': 10,
    'fine_tune_epochs': 15
}

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📈 Results & Visualizations

Model Comparison

Model	Accuracy	AUC	Parameters	Inference Time
Baseline CNN	90.0%	0.953	2.1M	15ms
MobileNetV2	94.2%	0.968	4.3M	18ms

Training Insights

• Smooth convergence
• No significant overfitting
• Validation metrics track training metrics closely
• Early stopping prevented unnecessary training

Grad-CAM Analysis

What it shows: 🔴 Red/Hot areas: High attention regions | 🟡 Yellow areas: Moderate attention | 🔵 Blue/Cool areas: Low attention

Clinical Relevance:

• Model focuses on lung infiltrates and opacities for pneumonia
• Normal X-rays show distributed, uniform attention
• Matches radiologist interpretation patterns

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🛠️ Technologies Used

Deep Learning & ML

TensorFlow 2.15 - Deep learning framework | Keras API - High-level neural networks API | MobileNetV2 - Pre-trained CNN architecture | NumPy 1.24 - Numerical computing | Pandas 2.1 - Data manipulation | scikit-learn 1.3 - ML utilities and metrics

Visualization

Matplotlib 3.8 - Plotting library | Seaborn 0.13 - Statistical visualizations | Grad-CAM - Model interpretability | OpenCV - Image processing

Web Development

Streamlit 1.29 - Web application framework | Pillow 10.1 - Image handling

Development Tools

Jupyter Notebook - Interactive development | Git & GitHub - Version control | VS Code - Code editor

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⚠️ Disclaimer

Important Notice

This application is for EDUCATIONAL and RESEARCH purposes only.

❌ NOT approved for clinical use | ❌ NOT a substitute for professional medical diagnosis | ❌ NOT validated on diverse populations

Limitations

1. Training Data Bias

• Trained on pediatric patients (1-5 years old)
• May not generalize to adult X-rays
• Limited to specific imaging conditions

2. Scope

• Binary classification only (Normal vs Pneumonia)
• Does not distinguish bacterial from viral pneumonia
• Cannot detect other lung conditions

3. Performance Variability

• Accuracy may vary with different X-ray machines
• Image quality affects predictions
• Real-world performance may differ from test set

Responsible Use

✅ Use as a screening tool in research settings | ✅ Always consult qualified healthcare professionals | ✅ Combine with clinical judgment and additional tests | ✅ Report any unusual predictions or behaviors

Ethical Considerations

This tool aims to assist, not replace, medical professionals. AI should augment human expertise, not substitute it.

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🚀 Future Improvements

Short-term (1-3 months)

• Multi-class classification (Bacterial/Viral/Normal)
• Model ensemble for higher accuracy
• Support for DICOM format
• Batch processing capability
• Export predictions to CSV/PDF

Medium-term (3-6 months)

• Deploy to cloud (AWS/GCP/Azure)
• REST API for integration
• Mobile app (TensorFlow Lite)
• User authentication system
• Prediction history tracking

Long-term (6-12 months)

• Explainable AI dashboard
• Active learning for continuous improvement
• Multi-modal inputs (patient history + X-ray)
• Integration with hospital PACS systems
• Clinical validation studies

📞 Contact

Author

Adhiraj Chakravorty

📧 Email: youradhi20@gmail.com | 💼 LinkedIn: https://www.linkedin.com/in/adhiraj-chakravorty-788685344/ | 🐱 GitHub: https://github.com/ADHIRAJ994

🎊 Thank you for checking out this project!

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