Automated pre-diagnosis of Acromegaly disease using Local Binary Patterns and its variants

Symptoms of some disease are seen on the face area. These symptoms make typical faces by giving typical characteristics to face areas and expressions. Hippocratic face, Parkinson face, Lupus face, Leprosy face can be given as example of these faces. One of the most typical ones of these faces is Acromegaly face. Acromegaly is a disease which occurs as a result of secretion of excessive amounts of growth hormone (GH). In this work, we propose a new and effective system that can pre-diagnose of Acromegaly automatically by the way of evaluating the patients face images. For this purpose, Local Binary Patterns (LBP) and its modified models Improved Local Binary Patterns (ILBP), Center Symmetric Local Binary Patterns (CS-LBP) are applied for feature extraction of face images. Weighted Chi-square, Euclidean and Manhattan classifiers are used for the classifying to the selected sets of features. Our results showed that LBP (8,1) coupled with Manhattan classifiers resulted in highest accuracy of 97%, sensitivity of 93%, specificity of 100% compared to other feature extraction techniques and classifiers. In this way, our proposed system is more suitable for diagnosis of Acromegaly disease with higher accuracy.