3 Kinds of Pneumonia

Dataset Curation Process

The initial dataset, comprising over 19,000 images, was refine using image similarity algorithms to remove duplicates, noisy images, and other defects. The Inception V3 model was employe to extract image embeddings, which were further analyze using unsupervise learning techniques to filter out images that exhibite poor quality or anomalies. Images exhibiting defects such as noise, pixelation, compression artifacts, and medical implants were systematically remove to ensure the dataset’s integrity.

Features of the Dataset

• Diverse Representation: The dataset provides X-rays for three distinct types of pneumonia, offering an ideal foundation for training AI models in medical diagnostics.
• Cleaned and Curated: All duplicate and faulty images have been removed, with the final dataset being subjected to quality control processes such as image clustering and manual review.
• Visualization and Disease Highlighting: Tools such as Inception V3 have been utilize to visually highlight abnormalities and disease characteristics, making the dataset highly suitable for visualization-base medical research.

Common Image Defects Addressed

Throughout the dataset cleaning process, several types of image defects were identify and addressed. These include:

1. Noise and Pixelation: Images with significant noise and pixelation were remove to enhance clarity.
2. Compression Artifacts: X-rays affected by excessive compression were exclude.
3. Medical Implants: X-rays with visible implants that might interfere with pneumonia diagnosis were filtered out.
4. Washed-out Images: Images with poor contrast or exposure were eliminated.
5. Side View and CT Images: Non-standard views and non-X-ray images, such as CT slices, were remove.
6. Aspect Ratio Distortion: Cropped or zoom images that distorted the aspect ratio were correct or exclude.
7. Annotated Images: X-rays with visible annotations or markings were remove.