"Leveraging MongoDB for Efficient Storage of MIMIC-IV CXR X-ray Images: A Research Perspective”

Abstract

Introduction: The exponential growth of medical imaging data presents a significant challenge for healthcare systems, necessitating scalable and efficient storage solutions. This research paper explores the utilization of MongoDB, a NoSQL database, for storing MIMIC-IV CXR (chest X-ray) images, a critical component of modern healthcare data.

Objectives: Leveraging MongoDB's capabilities for binary data storage, specifically its GridFS feature, this study investigates the feasibility and effectiveness of MongoDB as a storage solution for large-scale medical image datasets.

Methods: Through an in-depth analysis of MongoDB's features, performance metrics, and practical considerations, this paper provides valuable insights for healthcare researchers, practitioners, and database professionals seeking to optimize the management of medical imaging data. We also showcased the seamless integration of a MongoDB image database within the Python ecosystem, facilitated by PyMongo, to forecast pneumonia utilizing deep learning methodologies.

Results: This research verifies MongoDB's effectiveness in storing and handling big-scale medical imaging information through GridFS. It emphasizes the smooth integration of Python for real-time access and pneumonia prediction, providing significant insights into healthcare data management.

Conclusions: In summary, this research presents MongoDB's effectiveness in storing big medical image data with GridFS. It exhibits smooth integration with Python for real-time viewing and pneumonia prediction, providing valuable recommendations for maximizing healthcare data storage and analysis.