White Blood Cells Image Classification Based on Radiomics and Deep Learning

Abstract

White blood cell (WBC) identification performance is highly correlated with the quality of the extracted features, with radiomic features having greater resolution and more detailed information and deep features having more robust semantic information. This research integrates these two aspects to creatively suggest a WBC classification model based on radiomics and deep learning. This research suggests a brandnew method for extracting radiomic features from WBC images as well as a dual-branch feature fusion network RCTNet based on CNN and Transformer for extracting deep features. The radiomic feature extraction method not only has a simple segmentation algorithm but also solves the problem of cell adhesion, can obtain higher quality shape, color, and texture features without segmenting intact cells, and is more generalizable. RCTNet can extract more critical and recognizable deep features from WBC nuclei, avoiding the influence of too much redundant and invalid information on the results, and has better performance than several existing CNN models. We compared the classification outcomes based on radiomics with those based on deep learning to confirm the efficacy of the WBC classification model based on radiomics and deep learning suggested in this research. The experimental results demonstrated that combining radiomic features and deep features significantly improved the classification accuracy, with an AUC exceeding 0.9995, accuracy, sensitivity, specificity, precision, and F1-score reaching 0.9880, 0.9823, 0.9883, 0.9968, and 0.9881, respectively. The model has significant research significance in clinical applications and aids physicians in improving diagnosis and screening for diseases of WBCs.INDEX TERMS Radiomics, deep learning, feature fusion, dual-branch, white blood cell classification.

Blood Cell Attribute Classification Algorithm Based on Partial Label Learning

“…The distribution of blood cell categories after data cleaning is shown in Table 2. Based on the knowledge of blood cell morphology [26], we considered the diverse values each blood cell attribute might have. We constructed a set of reasonable candidate label sets for each category of blood cell based on the distribution of their attribute features.…”

“…However, the current CBM methods annotate attributes with only one value. According to hematological morphology [26], blood cells have multiple attributes. These attributes often have more than one value.…”

“…It is worth emphasizing that the blood cell dataset used in this study presents different partial label structures compared to the dataset the PICO algorithm uses. The blood cell morphology analysis [26] shows that blood cells of the same category can exhibit various attribute combinations. However, these combinations do not repeat across different blood cell categories.…”