# Blood Cell Revolution: Unveiling 11 Distinct Types with Naturalize' Augmentation
## 2 Related Works  
Within the realm of machine learning, the conventional approach involved the manual extraction of image features followed by classification. The advent of deep learning revolutionized this process by automating image classification, particularly impactful in blood cell analysis. Previous studies primarily concentrated on five white blood cell (WBC) types: neutrophils, eosinophils, basophils, lymphocytes, and monocytes. Distinguishing our research is the comprehensive classification of 11 distinct blood cell types, inclusive of segmented and banded neutrophils, introducing Meta-myelocbyte, Myelocyte, Pro-myelocye, erythroblasts, and platelets to the array. This expansion broadens the classification spectrum, redefining the scope of blood cell analysis. [PERSON] et al. [13] introduced W-Net, a CNN-based model for white blood cell (WBC) classification, achieving 97% accuracy with 10-fold cross-validation. Their work focused on classifying five WBC types: neutrophils, eosinophils, basophils, lymphocytes, and monocytes. [PERSON] et al. [14] combined VGG-16 and a feature reduction algorithm (SESSA) to achieve 95.67% accuracy for WBC leukemia image classification. They categorized WBCs into five classes. [PERSON] et al. [15] used ImageNet pre-trained architectures (VGG, ResNet, DenseNet) and data augmentation, with DenseNet-169 achieving 98.8% accuracy. Their study involved the classification of five WBC types. [PERSON] et al. [16] developed a segmentation algorithm and used SVM for WBC classification, achieving high accuracy in multiple datasets. They focused on five WBC types. [PERSON] et al. [17] proposed a hybrid deep model combining ResNet and DenseNet with a spatial and channel attention module (SCAM), outperforming previous methods. They worked with datasets containing five WBC types. [PERSON] et al. [18] applied transfer learning with ImageNet models, with MobileNet-V3-Small achieving 98.86% accuracy. They classified five WBC types. [PERSON] et al. [19] designed a CNN-based model using the Kaggle BCCD dataset, achieving competitive results. They also focused on five WBC types. [PERSON] et al. [20] introduced the EK-Means method for WBC image segmentation, achieving a validation accuracy of 96.24% with VGG-19. They categorized five WBC types. [PERSON] [21; 22] presented DLBCL models that used ResNet-50 for feature extraction and achieved impressive accuracy. They worked with datasets containing five WBC types. Other groups of researchers [23; 24; 25; 26; 27; 28] worked on the classification of 8 blood classes in the the PBC dataset [4]. Table 1 summarizes the work of those groups. Distinguishing our research is the expansion of blood cell classification into 11 distinct classes and the groundbreaking \"Naturalize\" augmentation technique. The shift from 8 to 11 classes presents a notable challenge due to the increased complexity in differentiating these new classes, particularly in cases such as pro-myelocbyte, myelocyte, and meta-myelocyte, where the number of available images is insufficient. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Paper** & **Year** & **Used Model** & **Classes** & \
\begin{tabular}{c} **Accuracy** \\ **(\%)** \\ \end{tabular} & **Precision** & **Recall (\%)** &
\begin{tabular}{c} **F1-Score** \\ **(\%)** \\ \end{tabular} \\ \hline
[23] & 2019 & Fine-Tuned VGG-16 & 8 & 96.20 & 97.00 & 96.00 & 97.00 \\
[24] & 2020 & ShuffleNet & 8 & 97.94 & 97.94 & 97.94 & 97.94 \\
[25] & 2021 & BloodCaps & 8 & 99.30 & 99.17 & 99.16 & 99.88 \\
[26] & 2021 & Ensemble ResNet-50-2 and VGG-19 & 8 & 99.51 & 99.47 & 99.58 & 99.93 \\
[27] & 2023 & CNN & 8 & 99.91 & 99.77 & 99.60 & 99.60 \\
[28] & 2023 & Mask R-CNN & 8 & 99.31 & 97.25 & 97.37 & 97.31 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Previous works on the classification of 8 blood classes of the PBC datasetThe \"Naturalize\" technique directly tackles two prominent challenges in deep learning: data insufficiency and class imbalance.