## 2 Related Works  
Numerous research studies and publications have focused on the autonomous image analysis of white blood cells in microscopic peripheral blood smears. These studies leveraged transfer-based learning from pre-trained ImageNet models across various dataset sizes [7; 8; 9; 10; 11; 12; 13; 14]. In their study [7], [PERSON] et al. employed convolutional neural networks, including a custom five-layer CNN (\"LeNet-5\"), and pre-trained models such as \"VGGs\", \"Inception V3\", and \"Xception\" for white blood cell classification. They tackled the challenging BCCD dataset, initially containing only 349 low-quality images distributed among four white blood cell categories: monocyte, lymphocyte, neutrophil, and eosinophil. Through extensive data augmentation techniques, they substantially expanded the dataset to over 3000 images per category. This augmented dataset was then divided into training and testing subsets, ultimately achieving an average classification accuracy of 87% for all four white blood cell types. In a similar vein, [PERSON] and [PERSON] [8] also used the BCCD dataset for object identification considering red blood cells (RBCs), white blood cells (WBCs), and platelets. They divided the BCCD dataset into 300 images for training, reserving the remaining images for testing. The authors employed the Tiny YOLO model for object identification and incorporated pre-trained CNNs like VGG-16, ResNet-50, Inception V3, and MobileNet. Notably, all types of white blood cells were identified as WBC cells without further classification. However, no single model excelled in the identification of all RBCs, WBCs, and platelets simultaneously. In another work [9], an automated system was introduced for classifying eight types of blood cells using convolutional neural networks (CNNs), specifically VGG-16 and Inceptionv3, with the PBC dataset. An impressive accuracy of 96.2% was achieved. [PERSON] et al. [10] introduced \"W-Net\", a CNN-based architecture for classifying five different types of white blood cells. They utilized a dataset from the Catholic University of Korea (CUK), comprising 6562 images of these five WBC types. Additionally, the LISC dataset was used, and the pre-trained ResNet model was included for comparison. The results demonstrated that W-Net achieved an average accuracy of 97%. In a different approach, a deep learning model using DenseNet121 was presented in [11] for classifying different white blood cell (WBC) types. The augmented BCCD dataset was employed, consisting of 12,444 images, including 3120 eosinophils, 3103 lymphocytes, 3098 monocytes, and 3123 neutrophils. Image pre-processing included the cropping of the WBC images. Then, augmentation techniques, such as flipping, rotation, brightness adjustment, and zooming, were applied to the isolated WBCs. The DenseNet121 model achieved an average accuracy of 98.84%. [PERSON] and colleagues [12] introduced a CNN-based architecture comprising five layers for classifying five types of white blood cells. They employed the BCCD dataset and applied augmentation techniques, including rotation, flipping, and shearing, to create a balanced training dataset with approximately 2500 images per class. The testing dataset contained fewer than 50 images, and the achieved average accuracy stood at an impressive 96.78%. In their study [13], [PERSON] and colleagues introduced a CNN-based architecture that utilized VGG-19 as a feature extractor and incorporated the Statistically Enhanced Salp Swarm Algorithm (SESSA) as an optimized classifier for categorizing five types of white blood cells (WBCs). Two datasets, the ALL-IDB and C-NMC datasets, were employed, resulting in an impressive average accuracy of 96.11%. [PERSON] and [PERSON] [14] employed transfer learning with pre-trained CNNs like \"ResNet\", \"DenseNet\", and \"VGG\" to classify five types of white blood cells (WBCs) using a small dataset called LISC, which contained 242 images.