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