White blood cells detection using deep learning in healthcare applications

Classifying and detecting the various types of White Blood Cells (WBCs) provides a valuable quantitative
depiction of a body’s health state and an important vision for the early treatment of illnesses. As a result, WBC
classification and detection are critical. Traditional microscopic examinations require extensive time and complex procedures, thus reducing their reliable statistical results. The detection method for White Blood Cells requires automatic accuracy, therefore presents an important advantage. Nevertheless, the similarity of WBC
samples and the insufficiency and imbalance of samples pose hurdles to automatic and accurate WBC categorization. This paper develops Op-YOLOv8 as an optimized YOLOv8 model designed to simultaneously analyze
many white blood cell types while maintaining efficiency. The Op-YOLOv8 utilizes Maxpooling with depth-wise
separable convolutions to compensate for feature loss through downsampling layers for maintaining vital
contextual information. The proposed micro-scale detection layer uses a shallower feature map structure to
deliver better YOLOv8 detection performance when dealing with overlapping and small-scale objects because it
gathers complete contextual information. According to the BCCD dataset-based experimental results, the OpYOLOv8 model performed better than other related models. Op-YOLOv8 demonstrates unrivaled WBC identification and detection performance through repeated validation of its maximum precision and recall alongside
F1-score measurements reaching 0.981, 0.989 and 0.985 respectively for all monitored WBC types. The model’s
detection capabilities stand strong because it delivers outstanding mAP50 and mAP50–95 results, particularly for
difficult classes Eosinophils and Neutrophils. The exceptional performance of Op-YOLOv8 stands out as it yields
better precision and recall and F1-score metrics than related models. The model provides ideal performance for
medical imaging scenarios requiring precise results. This model is a promising tool in medical imaging since it
displayed high precision in different scenarios, indicating a great potential for application in various healthcare
settings.