Improved white blood cells classification based on pre-trained deep learning models

Abstract

Leukocytes, or white blood cells (WBCs), are microscopic organisms that fight against infectious disease, bacteria, viruses and others. The manual method to classify and count WBCs is tedious, time-consuming and may have inaccurate results, whereas the automated methods are costly. This research aims to automatically identify and classify WBCs in a microscopic image into four types with higher accuracy. BCCD is the used dataset in this study, which is a scaled-down blood cell detection dataset. BCCD is firstly preprocessed by passing through various processes such as segmentation and augmentation; then, it is passed to the proposed model. Our model combines the advantage of deep models in automatically extracting features with the higher classification accuracy of traditional machine learning classifiers. The proposed model consists of two main stages: a shallow tuning pre-trained model and a traditional machine learning classifier on top of it. In this study, ten different pre-trained models with six types of machine learning are used. Moreover, the fully connected network (FCN) of pre-trained models is used as a baseline classifier for comparison. The evaluation process shows that the hybrid of MobileNet-224 as a feature extractor and logistic regression as classifier has a higher rank-1 accuracy of 97.03%. Besides, the proposed hybrid model outperformed the baseline FCN by 25.78% on average.