Diagnosis and Knowledge Discovery of Turner Syndrome Based on Facial Images Using Machine Learning Methods

Abstract

Turner syndrome (TS) is a chromosomal disorder disease that only affects the development of females. Based on TS facial images, we propose a new TS diagnosis model, which preserves the original ratio of face width to face height, extracts reliable features and conducts feature analysis based on support vectors (SVs) and principal components (PCs). The proposed TS model is composed of Image Preprocessing, Feature Extraction, Classifier Construction and Knowledge Discovery. For Image Preprocessing, by utilizing the techniques of face alignment, facial area intercept and brightness normalization, the original facial images are processed to the desired gray aligned facial area images, while the original ratio of face width to face height remains unchanged. For Feature Extraction, by employing the energy features of facial organ blocks and ratio features roughly and more finely, five reliable feature sets are extracted, i.e., Rough Energy Features, Finer Energy Features (FEF), Rough Ratio Features, Finer Ratio Features (FRF) and FRF2. For Classifier Construction, support vector machine (SVM), principal component analysis (PCA), kernel PCA (KPCA) and ensemble learning methods are used to establish 15 single classifiers (i.e., 5 SVM classifiers, 5 PCA+SVM classifiers and 5 KPCA+SVM classifiers) and 2 ensemble classifiers. The classifier established by the weighted voting method achieves the highest accuracy of 0.9127; FEF outperforms the other four feature sets. For Knowledge Discovery, the feature analysis based on SVs and PCs is carried out to discover important features. It is found that less energy of external canthus areas and a lower ratio of forehead height to forehead width often occur in TS patients through analyzing SVs, and the energy and ratio features of left zygoma area are important in identifying TS by analyzing PCs.