Nowadays, classification of imbalanced data is a major challenge in the machine learning (ML) algorithms, especially in medical data analysis, In this paper, deep learning algorithm which is the advance artificial neural network (ANN) is used for classifying five white blood cells (WBCs). Different preprocessing image techniques and algorithms are applied to isolate WBCs and segment the nucleus for the cytoplasm. Geometric, statistical and color features are extracted, the principal component analysis technique is applied to select the optimal features. The classification process has been repeated several times to tune the algorithm parameters and to find the best pattrens match through the training data in the learning process until achieve best classification accuracy. Multi-class classification results show high accuracy of more than 94% for the five types of WBCs. We evaluate the classification model using the geometric mean, Cohen’s Kappa, Receiver operating characteristic curve, Root mean squared error, relative absolute error and cross-validation techniques. The algorithm model achieves high accuracy and can conduct a multi-class classification of imbalanced datasets in terms of the above-mentioned metrics.