Child Pneumonia Dataset

Title: Child Pneumonia X-ray Dataset: A High-Quality Medical Imaging Resource for AI-Driven Diagnosis
Authors: MD. Abu Raihan, Syed Muntasin Fayaz, Aziza Haque, Afia Sarkar.
Affiliations: Department of Computer Science and Engineering, Khwaja Yunus Ali University, Sirajganj, Bangladesh .
Description: A chest X-ray is the most popular and widely recognized way to diagnose pneumonia. We made use of a collection of medical chest X-ray pictures that were obtained from Sirajganj, Bangladesh's North Bangle Medical Hospital and College. These pictures show inflammation of the lungs brought on by bacteria, viruses, or chemicals that irritate the lungs. A total of 282 X-ray images were collected from the various mobile devices used to take the pictures. We personally examined and eliminated photos with distracting backgrounds, low quality, motion blur, or other artifacts to guarantee high-quality data. In order to preserve the integrity of the dataset, we also carried out a thorough quality check procedure, removing any photos with high brightness, poor contrast, or fuzzy areas.
Subject Areas: Computer Sciences, Heath Science and Medical Science, Artificial Intelligence, Computer Vision and Pattern Recognition, Medical Imaging.
Data Format: JPG images (processed and filtered)
Data Collection: The devices used for capturing the images were the Xiaomi Redmi Note 7 and the Xiaomi Redmi Note 12. All raw images were captured under optimal lighting conditions and stored in JPG format. We portioned our dataset into 80:20 ratio for Training and Testing.