# Ghost-ResNeXt: An Effective Deep Learning Based on Mature and Immature WBC Classification
###### Abstract  
White blood cells (WBCs) must be evaluated to determine how well the human immune system performs. Abnormal WBC counts may indicate malignancy, tuberculosis, severe anemia, cancer, and other serious diseases. To get an early diagnosis and to check if WBCs are abnormal or normal, one needs to examine the numbers and determine the shape of the WBCs. To address this problem, computer-aided procedures have been developed because hematologists perform this laborious, expensive, and time-consuming process manually. Resultantly, a powerful deep learning model was developed in the present study to categorize WBCs, including immature WBCs, from the images of peripheral blood smears. A network based on W-Net, a CNN-based method for WBC classification, was developed to execute the segmentation of leukocytes. Thereafter, significant feature maps were retrieved using a deep learning framework built on GhostNet. Then, they were categorized using a ResNeXt with a Wildebeest Herd Optimization (WHO)-based method. In addition, Deep Convolutional Generative Adversarial Network (DCGAN)-based data augmentation was implemented to handle the imbalanced data issue. To validate the model performance, the proposed technique was compared with the existing techniques and achieved 99.16%, 99.24%, and 98.61% accuracy levels for Leukocyte Images for Segmentation and Classification (LlSC), Blood Cell Count and Detection (BCCD), and the single-cell morphological dataset, respectively. Thus, we can conclude that the proposed approach is valuable and adaptable for blood cell microscopic analysis in clinical settings. image classification; white blood cell; deep learning; W-Net; GhostNet +
Footnote †: journal:  
The typical method for diagnosing leukemia involves microscopy of peripheral blood smears, though alternative techniques are also employed. Traditional blood smear examination is a lengthier and more effort-intensive process [8; 9; 10; 11]. Further, such examination is vulnerable to several errors, including fatigue, operator experience, and significant inter- and intra-observer variation of standards. Manual inspection has a 30-40% error rate, based on the hematologist's experience level. Hence, an efficient technique is necessary for the fast diagnosis of the illness [12; 13; 14]. Challenges in the existing manual technique of identification can be overcome by an automated system, especially in the poorest countries, allowing for the standardized and effective detection of mature and immature WBCs. The automatic categorization of WBCs using computer vision algorithms has drawn significant scientific attention in digital image processing technology [15; 16; 17]. However, the categorization and identification of WBCs based on machine learning are difficult because of the morphological similarity among several subtypes and their structural abnormalities. In modern medical imaging, deep learning-based techniques are the most effective for identification and categorization tasks [18; 19; 20]. Moreover, they perform very well on large datasets. This paper classified normal WBCs into five categories and immature ones into seven categories. An efficient segmentation and feature extraction was carried out for this purpose using W-net and GhostNet. Thereafter, ResNeXt-based multi-class classification on WBC was carried out with the aid of Wildebeest Herd Optimization (WHO). Deep Convolutional Generative Adversarial Network (DCGAN)-based image augmentation was also employed to address the issues of imbalanced data and a lack of appropriate sample sizes. The key contributions of this study are summarized below:  
1. To improve the classifier's overall performance, we suggest a powerful W-net-based segmentation that can precisely find the WBC area. 2. We developed an efficient GhostNet-based deep learning technique to gather all high-level and low-level features of WBCs. 3. We employed ResNeXt with WHO to effectively classify atypical WBCs, including immature WBCs. 4.