White blood cells (WBCs) are a portion of the immune system which fights against germs. Leukemia is the most common blood cancer which may lead to death. It occurs due to the production of a large number of immature WBCs in the bone marrow that destroy healthy cells. To overcome the severity of this disease, it is necessary to diagnose the shapes of immature cells at an early stage that ultimately reduces the modality rate of the patients. Recently different types of segmentation and classification methods are presented based upon deep-learning (DL) models but still have some limitations. This research aims to propose a modified DL approach for the accurate segmentation of leukocytes and their classification. The proposed technique includes two core steps: preprocessing-based classification and segmentation. In preprocessing, synthetic images are generated using a generative adversarial network (GAN) and normalized by color transformation. The optimal deep features are extracted from each blood smear image using pretrained deep models i.e., DarkNet-53 and ShuffleNet. More informative features are selected by principal component analysis (PCA) and fused serially for classification. The morphological operations based on color thresholding with the deep semantic method are utilized for leukemia segmentation of classified cells. The classification accuracy achieved with ALL-IDB and LISC dataset is 100% and 99.70% for the classification of leukocytes i.e., blast, no blast, basophils, neutrophils, eosinophils, lymphocytes, and monocytes, respectively. Whereas semantic segmentation achieved 99.10% and 98.60% for average and global accuracy, respectively. The proposed method achieved outstanding outcomes as compared to the latest existing research works.

白细胞 (WBC) 是免疫系统的一部分，可以对抗细菌。白血病是最常见的血癌，可能导致死亡。它的发生是由于骨髓中产生大量破坏健康细胞的未成熟白细胞。为了克服这种疾病的严重性，有必要在早期诊断出未成熟细胞的形状，最终降低患者的模态率。最近基于深度学习 (DL) 模型提出了不同类型的分割和分类方法，但仍然存在一些局限性。本研究旨在提出一种改进的 DL 方法，用于准确分割白细胞及其分类。所提出的技术包括两个核心步骤：基于预处理的分类和分割。在预处理中，合成图像是使用生成对抗网络 (GAN) 生成的，并通过颜色变换进行归一化。使用预训练的深度模型（即 DarkNet-53 和 ShuffleNet）从每个血液涂片图像中提取最佳深度特征。通过主成分分析 (PCA) 选择更多信息特征并连续融合进行分类。利用基于颜色阈值和深度语义方法的形态学操作对分类细胞进行白血病分割。使用 ALL-IDB 和 LISC 数据集实现的分类准确率分别为 100% 和 99.70%，用于分类白细胞，即原始细胞、无原始细胞、嗜碱性粒细胞、中性粒细胞、嗜酸性粒细胞、淋巴细胞和单核细胞。而语义分割的平均准确率和全局准确率分别达到了 99.10% 和 98.60%。