The accurate classification of white blood cells and related blood components
is crucial for medical diagnoses. While traditional manual examinations and
automated hematology analyzers have been widely used, they are often slow and
prone to errors. Recent advancements in deep learning have shown promise for
addressing these limitations. Earlier studies have demonstrated the viability
of convolutional neural networks such as DenseNet, ResNet, and VGGNet for this
task. Building on these foundations, our work employs more recent and efficient
models to achieve rapid and accurate results. Specifically, this study used
state-of-the-art architectures, including MaxVit, EfficientVit, EfficientNet,
EfficientNetV2, and MobileNetV3. This study aimed to evaluate the performance
of these models in WBC classification, potentially offering a more efficient
and reliable alternative to current methods. Our approach not only addresses
the speed and accuracy concerns of traditional techniques but also explores the
applicability of innovative deep learning models in hematological analysis.

通过使用 MaxVit，EfficientVit，EfficientNet，EfficientNetV2 和 MobileNetV3 等最新的高效架构，本研究旨在评估这些模型在白细胞分类中的性能，为当前方法提供更高效、可靠的替代方案，并探索创新的深度学习模型在血液分析中的应用。