Improving blood cells classification in peripheral blood smears using enhanced incremental training

Peripheral Blood Smear (PBS) analysis is a vital routine test carried out by medical specialists to assess some health aspects of individuals. The automation of blood analysis has attracted the attention of researchers in recent years, as it will not only save time, money and reduce errors, but also protect and save lives of front-line workers, especially during pandemics. In this work, deep neural networks are trained on a synthetic blood smears dataset to classify fifteen different white blood cell and platelet subtypes and morphological abnormalities. For classifying platelets, a hybrid approach of deep learning and image processing techniques is proposed. This approach improved the platelet classification accuracy and macro-average precision from 82.6% to 98.6% and 76.6%-97.6% respectively. Moreover, for white blood cell classification, a novel scheme for training deep networks is proposed, namely, Enhanced Incremental Training, that automatically recognises and handles classes that confuse and negatively affect neural network predictions. To handle the confusable classes, we also propose a procedure called "training revert". Application of the proposed method has improved the classification accuracy and macro-average precision from 61.5% to 95% and 76.6%-94.27% respectively.