Chest X-ray Multi-Classification with CNN, ResNet, and Vision Transformers

This repository contains the code and resources for a comparative study of Convolutional Neural Networks (CNNs), Residual Networks (ResNet), and Vision Transformers for multi-classification in X-ray images.

Published Paper

You can also refer to our final paper for detailed results and discussion: Paper

Overview

Early and accurate detection of diseases is crucial for improving patient outcomes. This project focuses on utilizing deep learning techniques to classify chest X-ray images into different classes of cancerous cells. We compare the performance of CNNs, ResNet, and Vision Transformers to identify the most effective architecture for this task.

Dataset

We use the NIH Chest X-ray dataset, which comprises 112,120 X-ray images with disease labels from 30,805 unique patients. The dataset is publicly available and includes labels relevant to cancer diagnosis.

1. Navigate to data_download.ipynb and download the data. Ensure that it is downloaded into input folder.
2. Delete downloaded zip if needed
3. Change the path in models. image_dir root_dir and image_path, otherwise where applicable.

To run it on smaller dataset. Navigate to:

https://www.kaggle.com/datasets/nih-chest-xrays/sample

1. Download the files
2. Change the path in models. image_dir root_dir and image_path, otherwise where applicable.

Installation

To run the code, follow these steps:

1. Clone this repository:

git clone https://github.com/your-username/CSC413-Final-Project.git

2. Install the required dependencies:

pip install -r requirements.txt

Project Members

• Kaushik Murali
• Isha Surani
• Aviral Bhardwaj
• Ananya Jain

References

Include any relevant references, papers, or resources here.

License

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