Figure 1. Congenital Adrenal Hyperplasia (CAH) Classification Pipelines Using Handcrafted Features and Learned Representations.

Illustration of our CAH classification pipelines, including various preprocessing steps of the input image and using both handcrafted features and learned representations. A, The input image was preprocessed by automatically detecting the face region in the image, detecting the locations of the 68 facial landmarks, and aligning and cropping the face region. B, A total of 27 handcrafted features were calculated using the detected landmarks. C, Classical machine learning classifiers, such as random forests, were used to predict the CAH score based on the handcrafted features. D, A deep neural network was used to extract learned representations from the preprocessed image and predict the CAH score without predefined features. CVL indicates convolutional layer; FCL, fully connected layer.