Tuberculosis Chest X-rays Images

The Chest X-Ray Dataset comprises a total of 3,008 high-resolution images, categorized into two distinct groups for tuberculosis (TB) detection. This dataset is a valuable resource for researchers and developers working on medical imaging, computer vision, and AI-driven healthcare solutions.

Dataset Details

1. Tuberculosis (TB) Patients:

   • Number of Images: 2,494
   • Description: This category contains chest X-ray images from patients diagnosed with tuberculosis, showcasing various abnormalities commonly associated with the disease.

2. Normal Patients:

   • Number of Images: 514
   • Description: This category includes chest X-ray images of individuals with no diagnosed abnormalities, serving as a control group for training and testing purposes.

Applications

• AI-Powered TB Detection: Train machine learning and deep learning models to accurately identify tuberculosis from chest X-rays.
• Medical Imaging Research: Study patterns and abnormalities in X-ray images to improve diagnostic methods.
• Healthcare Innovation: Develop tools for early detection of TB in clinical settings, improving treatment outcomes.
• Educational Use: Train radiologists and healthcare professionals in identifying TB-related abnormalities.

Key Features

• High-Quality Data: All images are high-resolution, ensuring precise analysis and robust model training.
• Balanced Categories: Distinct grouping of TB-positive and normal cases for effective machine learning tasks.
• Ready for AI Integration: Optimized for use in deep learning frameworks and medical imaging pipelines.

Use Cases

• Disease Detection Models: Develop and evaluate AI models for tuberculosis detection.
• Medical Training Simulations: Create training tools for radiologists and medical students.
• Healthcare Automation: Implement AI in clinical workflows for faster and more accurate TB diagnosis.