WBC-KICNet: knowledge-infused convolutional neural network for white blood cell classification

Abstract White blood cells (WBCs) are useful for diagnosing infectious diseases and infections. Machine learning and deep learning have been used to classify WBCs from blood smear images. Despite advances in machine learning, there has been little research on applying medical domain knowledge to convolutional neural networks (CNNs) to improve WBC classification. The existing models are often inaccurate, rely on manual input, and fail to incorporate external medical knowledge into decision-making. This study used the blood cell count and detection dataset which contains images of monocytes, lymphocytes, neutrophils, and eosinophils for WBC classification. In this paper, we propose a CNN model for WBC classification called WBC-KICNet (knowledge-infused convolutional neural network). The present work uses two CNN models: the first model generates the knowledge vector from input images and the domain expert (hematologist); the second model extracts deep features from the input image. A feature fusion mechanism is then used to combine these two features to classify the WBCs. Several metrics have been used to evaluate the performance of the WBC-KICNet model. These measures yielded impressive results. Accuracy, precision, recall, specificity, and F1-score were rated 99.22%, 99.25%, 99%, 99.77%, and 99.25%, respectively. In each of the WBC classes, accuracy rates are: 98.7% for eosinophils, 99.83% for lymphocytes, 100% for monocytes, and 98.32% for neutrophils. As a result, the proposed WBC-KICNet classifies WBCs accurately and without much misclassification, and the results have been confirmed by a statistical hypothesis test ( t -test).