Automatic Classification of Turner Syndrome Using Unsupervised Feature Learning

Recently, the automatic diagnosis of Turner syndrome (TS) has been paid more attention. However, existing methods relied on handcrafted image features. Therefore, we propose a TS classification method using unsupervised feature learning. Specifically, first, the TS facial images are preprocessed including aligning faces, facial area recognition and processing of image intensities. Second, pre-trained convolution filters are obtained by K-means based on image patches from TS facial images, which are used in a convolutional neural network (CNN); then, multiple recursive neural networks are applied to process the feature maps from the CNN to generate image features. Finally, with the extracted features, support vector machine is trained to classify TS facial images. The results demonstrate the proposed method is more effective for the classification of TS facial images, which achieves the highest accuracy of 84.95%.