A Comprehensive High-Resolution Dataset for Analyzing Craniofacial Features in Goldenhar Syndrome: Images for Feature Detection and Medical Diagnosis
Description
This dataset contains 629 high-resolution images annotated with 900 labels across seven classes, specifically curated for craniofacial feature detection and analysis in Goldenhar Syndrome (GA). The dataset includes diverse annotations such as Cleft-Lip-and-Palate, Epibulbar Dermoid Tumor, Eyelid Coloboma, Facial Asymmetry, Malocclusion, Microtia, and Vertebral Abnormalities. Images are uniformly resized to 640x640 pixels and preprocessed with auto-orientation and histogram equalization to enhance contrast for improved feature detection. This dataset is an essential resource for researchers in craniofacial analysis, machine learning, and syndrome-specific diagnostics. It supports advancements in automated feature detection and clinical applications for GA. Its carefully curated structure and rich annotations make it suitable for academic research and real-world applications in automated craniofacial analysis.
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Institutions
- East West University