Principal component analysis (PCA) is based feature reduction that reduces the correlation of features. In this research, a novel approach is proposed by applying the PCA technique on various morphologies of red blood cells (RBCs). According to hematologists, this method successfully classified 40 different types of abnormal RBCs. The classification of RBCs into various distinct subtypes using three machine learning algorithms is important in clinical and laboratory tests for detecting blood diseases. The most common abnormal RBCs are considered as anemic. The RBC features are sufficient to identify the type of anemia and the disease that caused it. Therefore, we found that several features extracted from RBCs in the blood smear images are not significant for classification when observed independently but are significant when combined with other features. The number of feature vectors is reduced from 271 to 8 as time resuming in training and accuracy percentage increased to 98%.

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.

No abstract

Principal component analysis (PCA) is based feature reduction that reduces the correlation of features. In this research, a novel approach is proposed by applying the PCA technique on various morphologies of red blood cells (RBCs). According to hematologists, this method successfully classified 40 different types of abnormal RBCs. The classification of RBCs into various distinct subtypes using three machine learning algorithms is important in clinical and laboratory tests for detecting blood diseases. The most common abnormal RBCs are considered as anemic. The RBC features are sufficient to identify the type of anemia and the disease that caused it. Therefore, we found that several features extracted from RBCs in the blood smear images are not significant for classification when observed independently but are significant when combined with other features. The number of feature vectors is reduced from 271 to 8 as time resuming in training and accuracy percentage increased to 98%.

No abstract