EfficientNet - XGBoost: An Effective White-Blood-Cell Segmentation and Classification Framework

In the human body, white blood cells (WBCs) are crucial immune that help in early detection of a variety illnesses. Determination number WBCs can be used to diagnose conditions such as hematological, immunological, and autoimmune diseases, well AIDS leukemia. However, conventional method classifying counting is time-consuming, laborious, potentially erroneous. Therefore, this paper presents computer-assisted automated for recognizing detecting WBC categories from images. Initially, cell image preprocessed then segmented using an effective deep learning architecture called SegNet. Then, important features devised extracted EfficientNet architecture. Finally, categorized into four different types XGBoost classifier: neutrophils, eosinophils, monocytes, lymphocytes. The advantages SegNet, EfficientNet, make proposed model more robust achieve efficient classification WBCs. BCCD dataset evaluate performance methodology, findings compared existing state-of-the-art approaches based on accuracy, precision, sensitivity, specificity, F1-score. Evaluation results show approach has higher rank-1 accuracy 99.02% outperformed other techniques.