An Automated Approach to White Blood Cell Classification Using a Lightweight Convolutional Neural Network

White blood cell (WBC) count in our bloodstream plays a notable role in the diagnosis or prognosis of various blood diseases like acute lymphoblastic leukemia, heart diseases, or infections. While a radical change in white blood cell count comparative to the baseline provides us a sign that our body is being attacked by an antigen, it protects us from various infectious diseases as well. A particular type of white blood cell count tells us about being affected by a specific antigen. In medical diagnosis, it is of extreme importance to distinguish the different white blood cell components efficiently. An incorrect diagnosis may also become the cause of death. At present, in most of the medical centers, this classification is done manually by experts, which is time-consuming. Though some semi-automated procedures are being proposed where feature extraction is done manually before classifying automatically using microscopic blood smear images, it is still time-consuming and tedious. In recent years ANN, CNN-RNN, and also fused CNN models have been employed to perform the WBC classification. For making the procedure more effective, we propose a Deep Learning methodology to perform the classification applying a convolutional neural network model with a higher accuracy rate and a lower number of parameters compared to the state-of-the-art approaches.