A Multi-Class X-ray Image Dataset for Human Anatomy Classification

Description

This dataset consists of 1,256 high-quality, de-identified human body part X-ray images, structured and curated for multi-class image classification using computer vision and deep learning frameworks. The dataset represents real-world clinical variations, capturing different anatomical structures commonly requested in diagnostic imaging. The dataset is organized into seven distinct classes based on the specific anatomical body parts: Chest: 353 images Pelvis: 246 images Teeth: 231 images Leg: 151 images Hand: 99 images Neck: 91 images Head: 85 images Total Dataset Size: 1,256 images All images are formatted in standard image extensions (e.g., PNG/JPG) and resized appropriately to be readily compatible with popular deep learning architectures like ResNet, DenseNet, and MobileNet via Transfer Learning. Value of the Data: Anatomy Classification: It provides a reliable benchmark for developing robust pre-trained models that can automatically classify the orientation and anatomical region of an X-ray scan. Real-World Scenario: Reflecting authentic clinical environments, the distribution naturally contains class variations, offering an excellent dataset for benchmarking advanced loss functions, data augmentation techniques, and optimization algorithms in machine learning. Educational & Research Use: This dataset is highly valuable for final year design projects (FYDP), biomedical engineering researchers, and computer vision enthusiasts working on healthcare automation.

Institutions

• Daffodil International University

  Dhaka Division, Dhaka

Categories

Licence