ExDarkLBP: a hybrid deep feature generation-based genetic malformation detection using facial images

Body malformations, including those affecting the face, can arise as a result of genetic disorders. The diagnosis of such changes may often require specialist expertise, which is scarce. In this study, we have presented a computer vision model capable of accurately classifying malformed vs. non-malformed face images using automated classification techniques. Our model, which we refer to as ExDarkLBP (exemplar/patch-based feature extraction deploying pretrained DarkNet and local binary pattern), is based on exemplar hybrid feature engineering and incorporates two primary feature extraction methods: (i) textural feature generation using local binary pattern (LBP) and (ii) deep feature creation deploying pretrained DarkNet53. The most informative 500 textural and 500 deep features were first selected using the neighborhood component analysis (NCA) feature selection function and then merged to form a 1000 feature vector. This vector was subsequently fed to iterative NCA to choose the most valuable features. By combining this optimal feature vector with a support vector machine, we achieved an accuracy of 99.22% using a ten-fold cross-validation strategy. Our proposed ExDarkLBP model is highly accurate and may be potentially applied for the screening of facial malformations associated with genetic disorders using face images.