Üzen Hüseyin, Fırat Hüseyin
Department of Computer Engineering, Faculty of Engineering and Architecture, Bingol University, Bingol, Turkey.
Department of Computer Engineering, Faculty of Engineering, Dicle University, Diyarbakır, Turkey.
Health Inf Sci Syst. 2024 Apr 28;12(1):33. doi: 10.1007/s13755-024-00291-w. eCollection 2024 Dec.
White blood cells (WBC) play an effective role in the body's defense against parasites, viruses, and bacteria in the human body. Also, WBCs are categorized based on their morphological structures into various subgroups. The number of these WBC types in the blood of non-diseased and diseased people is different. Thus, the study of WBC classification is quite significant for medical diagnosis. Due to the widespread use of deep learning in medical image analysis in recent years, it has also been used in WBC classification. Moreover, the ConvMixer and Swin transformer models, recently introduced, have garnered significant success by attaining efficient long contextual characteristics. Based on this, a new multipath hybrid network is proposed for WBC classification by using ConvMixer and Swin transformer. This proposed model is called Swin Transformer and ConvMixer based Multipath mixer (SC-MP-Mixer). In the SC-MP-Mixer model, firstly, features with strong spatial details are extracted with the ConvMixer. Then Swin transformer effectively handle these features with self-attention mechanism. In addition, the ConvMixer and Swin transformer blocks consist of a multipath structure to obtain better patch representations in the SC-MP-Mixer. To test the performance of the SC-MP-Mixer, experiments were performed on three WBC datasets with 4 (BCCD), 8 (PBC) and 5 (Raabin) classes. The experimental studies resulted in an accuracy of 99.65% for PBC, 98.68% for Raabin, and 95.66% for BCCD. When compared with the studies in the literature and the state-of-the-art models, it was seen that the SC-MP-Mixer had more effective classification results.
白细胞（WBC）在人体抵御寄生虫、病毒和细菌的防御机制中发挥着重要作用。此外，白细胞根据其形态结构被分为不同的亚组。健康人和病人血液中这些白细胞类型的数量有所不同。因此，白细胞分类研究对医学诊断具有重要意义。近年来，由于深度学习在医学图像分析中的广泛应用，它也被用于白细胞分类。此外，最近引入的ConvMixer和Swin变压器模型通过获得有效的长上下文特征取得了显著成功。基于此，提出了一种新的多路径混合网络，用于结合ConvMixer和Swin变压器进行白细胞分类。这个提出的模型被称为基于Swin变压器和ConvMixer的多路径混合器（SC-MP-Mixer）。在SC-MP-Mixer模型中，首先，利用ConvMixer提取具有强空间细节的特征。然后Swin变压器通过自注意力机制有效地处理这些特征。此外，ConvMixer和Swin变压器模块由多路径结构组成，以便在SC-MP-Mixer中获得更好的补丁表示。为了测试SC-MP-Mixer的性能，在三个分别有4类（BCCD）、8类（PBC）和5类（Raabin）的白细胞数据集上进行了实验。实验研究结果显示，PBC的准确率为99.65%，Raabin为98.68%，BCCD为95.66%。与文献中的研究和最先进的模型相比，可以看出SC-MP-Mixer具有更有效的分类结果。
