CLP-NC: Comprehensive Dataset for Machine Learning-Based Morphological Analysis of Cleft Lip and Palate Variants Using Multimodal Medical Imaging

The Cleft Lip and Palate vs. Non-Cleft (CLP-NC) Image Dataset is a high-resolution dataset
designed for the automated detection and classification of cleft lip and palate anomalies. It
comprises 3,987 images, categorized into two distinct classes: Cleft Lip and Palate (CLP) and
Non-Cleft (NC). This dataset serves as a valuable resource for researchers in medical image
analysis, deep learning, and clinical decision-making.
Dataset Characteristics:
Total Images: 3,987
Number of Classes: 2
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 two classes:
- Cleft Lip and Palate (CLP): Congenital anomaly where the upper lip and/or palate fails to develop
properly.
- Non-Cleft (NC): Normal craniofacial structures without cleft-related deformities.
Dataset Structure and Splitting:
The dataset is divided into two main parts:
1. Non-Augmented Part (Used for Classification):
- Non-Augmented Imbalanced: Contains 168 images of Cleft Lip and Palate and 247 images of
Non-Cleft.
- Non-Augmented Balanced: Contains 500 images per class (Cleft Lip and Palate: 500, Non-Cleft:
500).
2. Augmented Part (Used for Object Detection):
- Augmented Imbalanced: Includes 1,132 augmented images with an imbalanced distribution.
- Augmented Balanced: Contains 1,440 images (Cleft Lip and Palate: 720, Non-Cleft: 720).
The dataset is split into:
- Training Set: 80%
- Validation Set: 10%
- Test Set: 10%