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%唇腭裂与非唇腭裂（Cleft Lip and Palate vs. Non-Cleft, CLP-NC）图像数据集是一款高分辨率数据集，专为唇腭裂异常的自动检测与分类任务设计。该数据集共包含3987张图像，分为两个明确类别：唇腭裂（Cleft Lip and Palate, CLP）与非唇腭裂（Non-Cleft, NC），可为医学图像分析、深度学习及临床决策领域的研究者提供宝贵的研究资源。 数据集特征： 图像总量：3987张 类别数量：2类 图像格式：JPG 图像分辨率：640×640像素 标注方式：所有图像均由医学专家手动标注并核验 数据预处理：采用自动取向校正与直方图均衡化处理，以增强特征检测效果 数据增强技术：涵盖旋转、缩放、亮度调节、翻转以及对比度调整 分类与标注： 本数据集包含两类图像： - 唇腭裂（CLP）：指上唇和/或腭部无法正常发育的先天性畸形 - 非唇腭裂（NC）：无腭裂相关畸形的正常颅面结构 数据集结构与拆分： 该数据集分为两大主要部分： 1. 非增强数据集（用于分类任务） - 非增强不平衡集：包含168张唇腭裂图像与247张非唇腭裂图像 - 非增强平衡集：每类各500张图像（唇腭裂：500张，非唇腭裂：500张） 2. 增强数据集（用于目标检测任务） - 增强不平衡集：包含1132张分布不平衡的增强图像 - 增强平衡集：共计1440张图像（唇腭裂：720张，非唇腭裂：720张） 该数据集的拆分比例为： - 训练集：80% - 验证集：10% - 测试集：10%