🫁 Pneumonia Detection from Chest X-ray

An end-to-end deep learning application for detecting Pneumonia from chest X-ray images using a fine-tuned ResNet18 model. The project demonstrates the complete machine learning workflow—from data preprocessing and model training to evaluation and deployment through a Streamlit web application.

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📌 Overview

Pneumonia is a potentially life-threatening lung infection where early diagnosis is critical. This project leverages transfer learning to classify chest X-ray images into one of two categories:

• ✅ Normal
• 🦠 Pneumonia

The primary objective is to build an accurate and user-friendly diagnostic assistance tool while showcasing an end-to-end machine learning pipeline.

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✨ Features

• Deep learning–based binary image classification
• Transfer learning with pretrained ResNet18
• Interactive web interface built with Streamlit
• Real-time inference on uploaded X-ray images
• Clean and modular project structure
• Easy-to-understand training and evaluation scripts

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🧠 Model Architecture

• Base Model: ResNet18 (ImageNet pretrained)
• Framework: PyTorch
• Transfer Learning: Fine-tuned final classification layer
• Output Classes: Normal, Pneumonia
• Loss Function: CrossEntropyLoss
• Optimizer: Adam

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📊 Model Performance

Metric	Score
Accuracy	~95%
Pneumonia Recall	~96%
False Negatives	32

Note: In medical image classification, Recall is often more important than overall accuracy because missing a pneumonia case (false negative) can have serious clinical consequences.

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🖥️ Application Preview

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📁 Project Structure

pneumonia-detection/
│
├── app.py                  # Streamlit application
├── src/
│   ├── data_loader.py
│   ├── model.py
│   ├── train.py
│   └── evaluate.py
│
├── requirements.txt
├── README.md
└── screenshot.png

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⚙️ Installation

Clone the repository:

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

Create a virtual environment (recommended):

python -m venv .venv

Activate the environment:

Windows

.venv\Scripts\activate

Linux / macOS

source .venv/bin/activate

Install dependencies:

pip install -r requirements.txt

Run the application:

streamlit run app.py

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📂 Dataset

This project uses the Chest X-ray Pneumonia Dataset available on Kaggle.

The dataset is not included in this repository due to its size and licensing restrictions.

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🚀 Training

To train the model from scratch or fine-tune the network:

python src/train.py

After training, save the model weights and use them with the Streamlit application for inference.

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📈 Evaluation

Model evaluation includes:

• Accuracy
• Recall
• Confusion Matrix
• Classification Metrics

The evaluation script can be executed using:

python src/evaluate.py

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⚠️ Repository Notes

• The trained .pth model file is not included because it exceeds GitHub's file size limit.
• You can reproduce the model by training it using the provided scripts.

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🔮 Future Improvements

• Confidence score for predictions
• Grad-CAM visualizations for model explainability
• Docker support
• Cloud deployment
• Model comparison (ResNet, EfficientNet, DenseNet)
• Improved recall with advanced augmentation techniques
• Continuous Integration (GitHub Actions)

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🛠️ Tech Stack

• Python
• PyTorch
• TorchVision
• Streamlit
• NumPy
• Pillow
• Matplotlib

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📚 Key Learnings

Through this project, I gained hands-on experience with:

• Transfer Learning
• Medical Image Classification
• Deep Learning using PyTorch
• Model Evaluation and Performance Analysis
• Building production-ready ML applications with Streamlit
• End-to-end machine learning workflows

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👨‍💻 Author

Abhishek Prajapati

• GitHub: https://github.com/Abhhiiissshhek
• LinkedIn: https://www.linkedin.com/in/abhishekprajapati-ml

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⭐ Support

If you found this project useful, consider giving the repository a star. Feedback, suggestions, and contributions are always welcome.