Classification of white blood cells using deep features obtained from Convolutional Neural Network models based on the combination of feature selection methods

Abstract

White blood cells are cells in the blood and lymph tissue produced by the bone marrow in the human body. White blood cells are an important part of the immune system. The most important task of these cells is to protect the human body against foreign invaders and infectious diseases. When the number of white blood cells in the blood is not enough for the human body, it can cause leukopenia. As a result of this situation, the resistance of the human body against infections and diseases decreases. In this respect, determining the number of these cells in the human body is a specialist task. Detection and treatment of this symptom is a labor-intensive process carried out by specialist doctors and radiologists. Image processing techniques have recently been widely used in biomedical systems for the diagnosis of various diseases. In this study, it is aimed to use image processing techniques to improve the classification performance of deep learning models in white blood cells classification. To perform the classification process more efficiently, the Maximal Information Coefficient and Ridge feature selection methods were used in conjunction with the Convolutional Neural Network models. The Maximal Information Coefficient and Ridge feature selection methods extracted the most relevant features. Afterward, the classification process was realized by using this feature set. In this study, AlexNet, GoogLeNet, and ResNet-50 were used as feature extractor and quadratic discriminant analysis was used as a classifier. As a result, the overall success rate was obtained as 97.95% in the classification of white blood cells. The experimental results showed that the use of the convolutional neural network models with feature selection methods contributed to improving the classification success of white blood cell types.