Pneumonia Detection Using InceptionV3

This repository contains the code and resources for detecting pneumonia from chest X-ray images using the InceptionV3 deep learning model. The project leverages PyTorch for model development and training.

---

Table of Contents

• Introduction
• Dataset
• Model
• Prerequisites
• Installation
• Usage
• Results
• Contributing
• License

---

Introduction

Pneumonia is a serious lung infection that can be life-threatening. Early detection is crucial for effective treatment. This project aims to automate the detection of pneumonia from chest X-ray images using a pre-trained deep learning model (InceptionV3), which has been fine-tuned for this specific task.

---

Dataset

• Source: The dataset used in this project is the Chest X-Ray Images (Pneumonia) dataset, available on Kaggle.
• Structure: The dataset is organized into three directories: train, val, and test, each containing subdirectories for NORMAL and PNEUMONIA images.
• Size: The dataset comprises thousands of X-ray images labeled as either NORMAL or PNEUMONIA.

---

Model

• Architecture: InceptionV3, a convolutional neural network known for its deep architecture and efficient computation.
• Modifications: The final fully connected layer is modified to output two classes (normal and pneumonia).
• Training: The model is trained using the Adam optimizer and Cross-Entropy Loss, with data augmentation applied to improve generalization.

---

Prerequisites

• Python 3.7 or higher
• PyTorch
• torchvision
• numpy
• pandas
• matplotlib
• seaborn
• scikit-image
• scikit-learn

---

Installation

1. Clone the repository:

   git clone https://github.com/your-username/pneumonia-detection-using-inceptionv3.git
   cd pneumonia-detection-using-inceptionv3

2. Create a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`

3. Download the dataset:

   • Download the dataset from Kaggle and place it in the appropriate directory as specified in the notebook.

---

Usage

1. Train the model:

   • Open the Jupyter notebook pneumonia-detection-using-inceptionv3.ipynb.
   • Run all cells to train the model on the provided dataset.

2. Evaluate the model:

   • The notebook includes code to evaluate the trained model on the test dataset and visualize the results.

3. Inference:

   • Use the trained model to predict pneumonia in new chest X-ray images by modifying the inference section in the notebook.

---

Results

• Accuracy: The model achieves a high accuracy on the test dataset, indicating its effectiveness in detecting pneumonia.
• Visualization: The notebook provides visualizations of predictions, including a comparison of actual and predicted labels.

---

Contributing

Contributions are welcome! Please fork the repository and submit a pull request with your improvements or new features.

1. Fork the repository
2. Create a new branch (git checkout -b feature-branch)
3. Commit your changes (git commit -am 'Add new feature')
4. Push to the branch (git push origin feature-branch)
5. Create a new pull request

---

License

This project is licensed under the MIT License. See the LICENSE file for more details.