# Ghost-ResNeXt: An Effective Deep Learning Based on Mature and Immature WBC Classification
## 4 Result and Discussion
### BCCD dataset:  
The Blood Cell Count Detection (BCCD) dataset [27] is publicly available on Kaggle, an online community platform for data scientists and machine learning enthusiasts. It contains cell type designations in a CSV file and 12,453 JPEG images of leukocytes. Neutrophil, monocyte, lymphocyte, and eosinophil are the four cell types included in this collection. In contrast to the 207, 21, 33, and 88 original pictures, there are 3123, 3107, 3103,  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset** & **Pixel Size** & \multicolumn{2}{c}{**Number of WBC**} & \multicolumn{2}{c}{**Total Number of Images**} & \multicolumn{2}{c}{**Staining**} & \multicolumn{2}{c}{**Microscopic and Zoom**} & \multicolumn{1}{c}{**Camera**} \\ \hline \multirow{4}{*}{BCCD} & \multirow{4}{*}{320 \(\times\) 240} & Neutrophil & 3123 & \multirow{4}{*}{12,444} & \multirow{4}{*}{Gismo-right} & Regular light & \multirow{4}{*}{CCD color camera} \\ \cline{3-4} \cline{6-7}  & & Monocyte & 3098 & & & & \\ \cline{3-4} \cline{6-7}  & & Lymphocyte & 3103 & & & & \\ \cline{3-4} \cline{6-7}  & & Eosinophil & 3120 & & & & \\ \hline \multirow{4}{*}{LISC} & \multirow{4}{*}{720 \(\times\) 576} & Neutrophil & 50 & \multirow{4}{*}{242} & \multirow{4}{*}{Gismo-right} & \multirow{4}{*}{Axioskope40} \\ \cline{3-4} \cline{6-7}  & & Monocyte & 48 & & & \\ \cline{3-4} \cline{6-7}  & & Lymphocyte & 52 & & & \\ \cline{3-4} \cline{6-7}  & & Eosinophil & 39 & & & \\ \cline{3-4} \cline{6-7}  & & Basophils & 53 & & & \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Single Cell \\ dataset \\ \end{tabular} } & \multirow{4}{*}{400 \(\times\) 400} & Myelocyte & 42 & \multirow{4}{*}{3517} & \multirow{4}{*}{Papanicolaou stain} & \multirow{4}{*}{M8 digital micro-} \\ \cline{3-4} \cline{6-7}  & & Metamaplecytes & & & & \\ \cline{3-4} \cline{6-7}  & & Bilobed & & & & \\ \cline{3-4} \cline{6-7}  & & Promyelocytes & & & & \\ \cline{3-4} \cline{6-7}  & & Myeloblast & 3268 & & & \\ \cline{3-4} \cline{6-7}  & & Promyelocytes & & & & \\ \cline{3-4} \cline{6-7}  & & Monoblast & 26 & & & \\ \cline{3-4} \cline{6-7}  & & Erythroblast & 78 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Properties of three datasets.  
and 3120 augmented pictures, respectively. The dataset excludes basophils because they typically comprise less than 1% of all leukocytes.  
**LISC dataset:**  
Tehran University's Hematology-Oncology Research Centre (Iran) and BMT Research Center of Imam Khomeini hospital in Tehran, Iran provided the LISC dataset [28]. It contained 250 photos of ground truth as well as hematological images obtained from 400 participants on 100 microscopic images. The acquired photos are 720,576 pixels in size and the.bmp file type. Medical experts categorize the dataset into five subcategories using multiple photos, including fifty-three basophils, thirty-nine eosinophils, fifty neutrophils, forty-eight monocytes, and fifty-two lymphocytes.  
**Single-cell morphological dataset:**  
The single-cell morphological dataset (AML Cytomorphology LMU) [29] used in this study included WBCs from patients with AML and from healthy people. The dataset was created by the Munich University Hospital, using 18,365 single-cell images identified by experts and gathered from peripheral blood samples of 100 AML patients and 100 healthy individuals between 2014 and 2017. There were 15 different single-cell image categories created from the collection. In that, seven classes were leukemic WBC myelocytes, metamyelocytes, bilobed promyelocytes, myeloblasts, promyelocytes, monoblasts, and erythroblasts, whereas the rest were normal WBCs. Based on a recognized morphological classification, professional analyzers assessed the cancer and non-cancerous WBCs.