Detection and classification of white blood cells with an improved deep learning-based approach

The analysis of white blood cells, which defend the body against deadly infections and disease-causing substances, is an important issue in the medical world. The concentrations of these cells in the blood, examined in 5 classes, i.e. monocytes, eosinophils, basophils, lymphocytes, and neutrophils, vary according to the types of diseases in the body. The peripheral blood smear is widely used to analyze blood cells. Manual evaluation of this method is laborious and time-consuming. At the same time, many environmental and humanistic parameters affect the method's performance. Therefore, in the presented study, a real-time detection process is realized. Firstly, YOLOv5s, YOLOv5x, and Detectron 2 R50-FPN pretrained models in the object recognition framework are used. Next, two original contributions are made to the study to improve the model's performance. The first contribution includes optimizing the activation function, an essential criterion in training the model, and an arrangement provided in the architecture. With this proposed approach, an improvement of 0.006 is achieved in the recognition rates of all classes. The second contribution is the combined use of the YOLO and Detectron2 frameworks, which have two different object evaluation processes. The success rate achieved with this hybrid structure provided an improvement between 3.44% and 14.7% compared to the outputs obtained from the YOLO and Detectron2 pretrained models. In addition, the maximum accuracy rate of this hybrid structure on the test dataset for detection and classification of white blood cells is obtained as 98%.