ACromegaly Pre-Diagnosis Based On Principal Component And Linear Discriminant Analysis

Acromegaly is a disease which occurs as a result of secretion of excessive amounts of growth hormone. Today, accurate diagnosis of Acromegaly can be diagnosed using the result of clinical and biochemical tests and measurements. Before applying these tests, the experts use the physical appearance of patience to diagnose. As a result of this, pre-diagnosis varies depending upon the experience of the doctors and patients are subjected to lots of necessary and unnecessary tests. In this study, using the patients face images, a new and efficient software has proposed which automatically diagnoses Acromegaly invariant of age, gender and facial expression. For this purpose, after applying various pre-processing to face images, Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Subspace Linear Discriminant Analysis (Subspace LDA) are used to extract features and Euclidean and Manhattan classifiers are used to classify the obtained features. Our results showed that TBA+DAA coupled with Euclidean resulted in highest accuracy of 96%, sensitivity of 100%, specificity of 95% compared to other feature extraction techniques.