At first, RBCs are visualized by off-axis DHM and the quantitative phase images are reconstructed by the numerical algorithm.37,38 Then, single RBCs are extracted from images with multiple RBCs using the watershed algorithm.39 At the next step, following 2-D features of projected surface area (PSA), perimeter, radius, elongation, and PSA to perimeter rate are extracted. In this paper, we have ignored extracting 2-D features related to the inner section of RBC, unlike the previous method proposed by Refs. 25, 26, since flat-disc and echinospherocyte RBCs do not have the inner section. Also, volume, surface area, SAV ratio, average RBC thickness, sphericity index, sphericity coefficient and functionality factors, and MCH and MCH surface density (chemical properties of RBC) are extracted from single RBCs. The latter feature-set is related to the morphological and biochemical properties of RBC 3-D profile. Along with the 3-D features, two new features related to the ring section of RBC are introduced. These features add significant information to the classification model and increase the discrimination power of the classifier. Then, each feature set is fed into PRNN, separately, and the classification results are compared using 10-fold cross validation (CV) technique. Since we are involved in a classification model with nonlinear decision boundary, we have decided to use PRNN strategy. In PRNN, the training algorithm is Bayesian regulation back-propagation, which updates the weights according to Levenberg–Marquardt optimization technique and the activation function for midlevel layers is hyperbolic tangent sigmoid.