Microscopic Blood Cell Classification Using Inception Recurrent Residual Convolutional Neural Networks

Abstract

Deep Learning (DL) approaches have been explored in different modalities of biomedical image analysis, and they provide superior performance against alternative machine learning approaches. In this paper, we have evaluated the performance of a deep learning model called the Inception Recurrent Residual Convolutional Neural Network (IRRCNN) for White Blood Cell (WBC) and Red Blood Cell (RBC) classification. We have tested the performance of the IRRCNN approach on two publicly available blood cell datasets for both RBC and WBC classification obtained from the Yale School of medicine and CellaVision respectively. The experimental results show almost 100% recognition accuracy for the WBC dataset and 99.94% testing accuracy for RBC classification. This is approximately a 1.4% and 2.35% improvement when compared to existing deep learning-based approaches.

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