# Ghost-ResNeXt: An Effective Deep Learning Based on Mature and Immature WBC Classification
## 2 Literature Review  
[PERSON] et al. [21] suggested a hybrid strategy for classifying WBCs and first pre-processed the input dataset before inputting it via feature extraction and feature fusion, utilizing a transfer learning step. This was done by using DarkNet53 and DenseNet201--two deep learning methods. An entropy-controlled Marine Predators Algorithm was then used to choose the best attributes. In terms of several performance indicators, this strategy was compared with other existing methods. A two-stage deep learning model was developed by [PERSON] et al. [22] to categorize abnormal WBCs. To create different synthetic WBC pictures, an initial augmentation technique based on geometric modification and the Deep Convolutional Auto-Encoder (DCAE) generating model was presented. Further, the WBCs were divided by utilizing the framework of CMYK-Moment Localization-Feature Fusion Extraction. Then, binary and multi-class classifications were carried out using a hybrid DCAE/Convolutional Neural Network (CNN). Using DCAE, the image was first transformed into a refined version. Then it was supplied into the CNN for further extraction of features. The model's outputs were compared against existing techniques to classify abnormal WBCs. [PERSON] et al. [23] classified four blood cell categories, including eosinophils, neutrophils, monocytes, mononuclear, and lymphocytes, using a multi-level hybrid system. A Faster R-CNN network was used at the first level to identify the region of interest in WBCs and to distinguish polymorphonuclear from mononuclear cells. On being separated, the second-level subclasses were recognized using two adjacent CNNs in the MobileNet framework. The suggested model presented the results of Monte Carlo cross-validation according to F1-score, precision, recall, and accuracy. Using a random forest approach, [PERSON] et al. [24] created a model that could precisely identify and divide immature leukocytes into four categories: myeloblasts,romyelocytes, monoblasts, and erythroblasts. To acquire the masking of the cytoplasm and nuclei, a segmentation method based on multi-Otsu thresholding was used. The feature extraction approach was used to obtain 16 cytomorphological characteristics. Finally, using the retrieved features as a basis, the random forest algorithm performed the classification where accuracy, precision, recall, and specificity measurements were applied to evaluate performance. For WBC segmentation, [PERSON] et al. [25] developed a multi-scale information fusion network (MIF-Net) with external and internal procedures for fusing spatial information; MIF-Net is a deep network. To fuse external information, the MIF-Net separated and reproduced the boundary information on multiple scales. For internal fusion, the splitter also transmitted spatial data. It ensured network-wide feature empowerment. After going through a few stages of processing, internal information fusion was finally combined with the fusion of external data. Outcome masks were ultimately created using fused characteristics. On four publicly accessible datasets, the performance assessment was conducted. [PERSON] et al. [26] presented two deep models for the combined segmentation of nuclei and cytoplasm in WBC pictures: the leukocyte deep aggregation segmentation network (LDAS-Net) and the leukocyte deep segmentation network (LDSNet). For feature extraction, only three down-sampling stages were included in the LDS-Net. To minimize the loss of spatial information and transmit low-level information, the improved version of the LDS-Net-LDAS-Net was presented. To combine low-level data with downscaled spatial features in the LDAS-Net, a dense feature concatenation block was used. Finally, the overall summary of the literature review is presented in Table 1.