Classification of Blood Cells with Convolutional Neural Network Model

Abstract

White Blood Cells are the primary blood cells that come from the bone marrow and are essential for constructing our body's defense system. Leukopenia is a disorder where the body's capacity to fight off infections is compromised due to a low white blood cell count. White blood cell counting is a specialty procedure that is usually carried out by experts and radiologists. Thanks to recent advances, image processing techniques are frequently used in biological systems to identify a wide spectrum of illnesses. In this work, image processing techniques were applied to enhance the white blood cell deep learning models' classification accuracy. To expedite the classification process, Convolutional Neural Network models were combined with Ridge feature selection and Maximal Information Coefficient techniques. These tactics successfully determined the most important characteristics. The selected feature set was then applied to the classification procedure. ResNet-50, VGG19, and our suggested model were used as feature extractors in this study. The categorizing of white blood cells was completed with an amazing 98.27% success rate. Results from the experiments demonstrated a considerable improvement in classification accuracy using the proposed Convolutional Neural Network model.