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Version 2

A Comprehensive High-Resolution Dataset for Analyzing Craniofacial Features Syndrome: Images for Feature Detection.

Published:29 January 2025|Version 2|DOI:10.17632/ffsthxyp4d.2
Contributors:Israt Jahan,

Description

This dataset contains-resolution images, specifically curated for craniofacial feature detection and analysis in Goldenhar Syndrome (GA). 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.

Categories

Computer Vision, Medical Imaging, Clinical Research

Licence

Creative Commons Attribution 4.0 International

Version 3

Goldenhar-CFID: A Novel Dataset for Craniofacial Anomaly Detection in Goldenhar Syndrome

Published:28 February 2025|Version 3|DOI:10.17632/ffsthxyp4d.3
Contributors:
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Description

The Goldenhar Syndrome Craniofacial Image Dataset (Goldenhar-CFID) is a high-resolution dataset designed for the automated detection and classification of craniofacial abnormalities associated with Goldenhar Syndrome (GS). It comprises 4,483 images, categorized into seven distinct classes of craniofacial deformities. This dataset serves as a valuable resource for researchers in medical image analysis, deep learning, and clinical decision-making. Dataset Characteristics: Total Images: 4,483 Number of Classes: 7 Image Format: JPG Image Resolution: 640 x 640 pixels Annotation: Each image is manually labeled and verified by medical experts Data Preprocessing: Auto-orientation and histogram equalization applied for enhanced feature detection Augmentation Techniques: Rotation, scaling, brightness adjustments, flipping, and contrast modifications Categories and Annotations The dataset includes images categorized into seven craniofacial deformities: Cleft Lip and Palate – Congenital anomaly where the upper lip and/or palate fails to develop properly. Epibulbar Dermoid Tumor – Benign growth on the eye’s surface, typically at the cornea-sclera junction. Eyelid Coloboma – Defect characterized by a partial or complete absence of eyelid tissue. Facial Asymmetry – Uneven development of facial structures. Malocclusion – Misalignment of the teeth and jaws. Microtia – Underdeveloped or absent outer ear. Vertebral Abnormality – Irregular development of spinal vertebrae. Dataset Structure and Splitting The dataset consists of four main subdirectories: Original – Contains 547 raw images. Unaugmented Balanced – Contains 210 images per class. Augmented Unbalanced – Includes 4,483 images with augmentation. Augmented Balanced – Contains 756 images per class. The dataset is split into: Training Set: 80% Validation Set: 10% Test Set: 10%

Institutions

Institutions

East West University

Categories

Biochemical Disorders Genetics, Diagnosis, Object Detection, Deep Learning

Licence

Creative Commons Attribution 4.0 International