Chest X-Ray Abnormality Detection Project

This repository contains the code, documentation, and resources for a project focused on the detection of abnormalities in chest X-ray images using deep learning techniques. The project leverages state-of-the-art convolutional neural networks (CNNs) and transfer learning to assist in the diagnosis of diseases such as pneumonia and COVID-19 from radiographic images.

Project Overview

• Goal: Develop and evaluate a computer-aided detection system for chest X-rays, aiming to support radiologists in identifying thoracic diseases.
• Techniques: Utilizes deep learning, specifically CNNs, for image classification and abnormality localization.
• Datasets: References to large-scale chest X-ray datasets and benchmark studies are included in the papers/ directory.
• Documentation: The editable_source/ folder contains editable DOCX versions of the main report and configuration manual.

Repository Structure

• adok18191592.pdf — Main project report (PDF)
• Configuration Manual x18191592.pdf — Configuration and setup manual (PDF)
• editable_source/ — Editable DOCX sources for the report and manual
• papers/ — Collection of referenced research papers
• link.txt — Links to demonstration videos (YouTube and Google Drive)
• x18191592_feedbackQA.docx, x18191592_viva_response.pdf, Final Feedback from Examiner.pdf — Feedback and responses

Demo Video

A demonstration video of the system is available:

• YouTube Link
• Google Drive Link

Example Images

Below are example images to illustrate the system's functionality. Replace these placeholders with actual output or system screenshots as needed.

Sample input X-ray image.

Example of model prediction and abnormality localization.

Getting Started

1. Clone the repository:

   git clone <repo-url>

2. Review the configuration manual in Configuration Manual x18191592.pdf for setup instructions.
3. Refer to the main report in adok18191592.pdf for methodology, results, and discussion.

References

A curated list of research papers relevant to chest X-ray analysis and deep learning is provided in the papers/ directory.

Feedback

See Final Feedback from Examiner.pdf and x18191592_feedbackQA.docx for examiner feedback and responses.

---

For more details, consult the full report and configuration manual. For questions, please refer to the contact information in the documentation.