<p>The accuracy of the MobileNetv4 models.</p>

Cancer occurs when healthy cells in the body grow abnormally and out of control. Leukemia is a type of cancer that affects White Blood Cells (WBCs) and can cause a lethal infection and early death. Identification and classification of different types of leukemia are performed manually and automatically. The doctors analyze blood samples under a microscope and consider any changes in the number and structure of WBCs as a sign of cancer in the manual method. It is a time-consuming, inaccuracy-prone process that depends on the expertise and skill of the physician and the type of laboratory equipment. In recent years, more automated methods of identifying and classifying leukemia have been developed with the help of Artificial Intelligence (AI) and Computer Vision (CV), with the aim of overcoming the challenges of manual approaches. This paper introduces two types of attention blocks, Parallel Cognitive Attention Block (PCAB) and Sequential Cognitive Attention Block (SCAB), to integrate into the architecture of any Convolutional Neural Network (CNN). Each of the proposed attention blocks is composed of the channel and spatial attention sub-blocks. They extract the structure and location of WBCs in the feature maps, similar to the ventral and dorsal streams in the human brain. The PCAB and SCAB were embedded in the architecture of the ResNet18 and MobileNetv4. The baseline and attention-based networks are trained, validated, and tested by two types of data splitting on the four leukemia datasets, including ALL, ALL-IDB2, C-NMC, and Mixture-Leukemi (ALL-IDB2+Munich AML Morphology), with the same experimental conditions for 30 epochs. The classification results demonstrate that the proposed model (MobileNetv4PCAB) achieved better performance metrics than others on all datasets in the test steps. It showed that the suggested model achieved the accuracy values of 100%, 100%, 93.61%, and 99.4%, and the F1-score values of 100%, 100%, 95.64%, and 99.3% with ALL, ALL-IDB2, C-NMC, and Mixture-Leukemia datasets, respectively. We confirmed that the proposed model outperforms existing state-of-the-art methods.当体内健康细胞出现异常增殖并失去控制时，便会引发癌症。白血病是一类累及白细胞（White Blood Cells, WBCs）的癌症，可引发致命感染并导致早逝。不同类型白血病的识别与分类可通过手动与自动两种方式完成：手动方法中，医师需在显微镜下分析血液样本，并将白细胞数量与结构的异常变化视作癌症征兆；该过程耗时费力且易产生误差，其结果依赖于医师的专业经验与技能，以及实验室设备的类型。近年来，借助人工智能（Artificial Intelligence, AI）与计算机视觉（Computer Vision, CV）技术，各类用于白血病识别与分类的自动化方法相继被提出，旨在解决手动方法面临的诸多挑战。本文提出两类注意力模块：并行认知注意力模块（Parallel Cognitive Attention Block, PCAB）与序列认知注意力模块（Sequential Cognitive Attention Block, SCAB），可集成至任意卷积神经网络（Convolutional Neural Network, CNN）的架构中。所提的两类注意力模块均由通道注意力子模块与空间注意力子模块构成，可在特征图中提取白细胞的结构与位置信息，其运作机制类似于人脑的腹侧与背侧视觉通路。本文将PCAB与SCAB嵌入至ResNet18与MobileNetv4的网络架构中。在包含ALL、ALL-IDB2、C-NMC以及混合白血病数据集（Mixture-Leukemi，即ALL-IDB2+慕尼黑AML形态学数据集）的四类白血病数据集上，本文采用两种数据划分策略，在完全一致的实验条件下对基线网络与基于注意力机制的网络开展训练、验证与测试，训练轮次均为30个。分类结果显示，所提模型（MobileNetv4PCAB）在所有数据集的测试阶段均取得了优于其他模型的性能指标。针对ALL、ALL-IDB2、C-NMC与混合白血病数据集，该模型的准确率分别为100%、100%、93.61%与99.4%，F1分数分别为100%、100%、95.64%与99.3%。本文证实，所提模型的性能优于当前已有的前沿方法。