Recognition of Leukocytes using a Combination of Deep Features and Ensemble Classifier

Detection of the peripheral blood plays a vital role in the field of medical diagnostics and control of major diseases. In this regard, detecting leukocytes or White Blood Cells (WBCs) is important. Leukocytes have five classes: Basophils, Eosinophils, Lymphocytes, Monocytes, and Neutrophils. The dataset used for this study is Raabin-WBC, containing leukocyte images. Deep learning techniques are applied to the dataset for the detection of mentioned leukocyte classes. In this case, to remove class imbalance, oversampling/replication of data is done. Transfer learning is applied in which a pre-trained ResNet18 model gets adapted to new data. Deep features are extracted from the pool5 layer of the network and are fed to the Ensemble Bagged Trees classifier. A remarkable classification accuracy of 99.5% is achieved through 5-fold cross-validation.