TransMixNet: An Attention Based Double-Branch Model for White Blood Cell Classification and Its Training with the Fuzzified Training Data

Chen, Hua; Liu, Juan; Hua, Chunbing; Zuo, Zhiqun; Feng, Jing; Pang, Baochuan; Xiao, Di

doi:10.1109/bibm52615.2021.9669587

Cited by 6 publications

(2 citation statements)

References 16 publications

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“…The great majority of methods described do not propose both segmentation and classification, and no one addressed the model degradation or the overfitting of larger models in the white blood cell count problem, which has only five classes. [30][31][32][33] Here we present a comparison between different ResNet models, ranging from 18 to 152 layers, using both standard (18 and 34 layers) and bottleneck convolutions (50, 101, and 152). We can see from the learning curves (Fig.…”

Section: Discussionmentioning

confidence: 99%

White blood cells segmentation and classification using a random forest and residual networks implementation

Rodrigues Garcia,

Ponce Ayala,

Pratavieira

et al. 2024

Computational Optical Imaging and Artificial Intelligence in Biomedical Sciences

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Artificial intelligence algorithms are interesting solutions to automate the tedious manual counting of white blood cells by a specialist. Although interesting machine learning algorithms have been proposed for this task, there is a lack in the literature for high-accuracy methods (more than 99%) tested on larger datasets (more than 10 thousand images). This paper presents a segmentation and classification methodology, based on Random Forest and ResNet50, along with a comparison between ResNet models with different numbers of layers. The segmentation was tested in microscope-like images mounted using multiple single-cell images, widely available in online datasets, yielding 300×300 images to be classified by the residual network. For image classification, ResNet50 reached higher accuracies (99.3%, to the best of our knowledge, the higher accuracy for models with more than 1000 images), with the model size comparison pointing to model overfitting for larger models.

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Section: Discussionmentioning

confidence: 99%

White blood cells segmentation and classification using a random forest and residual networks implementation

Rodrigues Garcia,

Ponce Ayala,

Pratavieira

et al. 2024

Computational Optical Imaging and Artificial Intelligence in Biomedical Sciences

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“…Then, a region-growing algorithm was employed to generate the segmentation results. Chen, et al [14] employed two pre-trained CNN models and fused their feature maps by addition. Then, a squeeze and excitation module was embedded in their model to enhance the representation learning.…”

Section: Introductionmentioning

confidence: 99%

Shifted Window Vision Transformer for Blood Cell Classification

Chen

Wang

et al. 2023

Electronics

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Blood cells play an important role in the metabolism of the human body, and the status of blood cells can be used for clinical diagnoses, such as the ratio of different blood cells. Therefore, blood cell classification is a primary task, which requires much time for manual analysis. The recent advances in computer vision can be beneficial to free doctors from tedious tasks. In this paper, a novel automated blood cell classification model based on the shifted window vision transformer (SW-ViT) is proposed. The SW-ViT architecture is firstly pre-trained on the ImageNet dataset and fine-tuned on the blood cell images for classification. Two transfer strategies are employed to generate better classification results. One is to fine-tune the entire SW-ViT, and the other is to only fine-tune the linear output layer of the SW-ViT while all the other parameters are frozen. A public dataset named BCCD_Dataset (Blood Cell Count and Detection) is utilized in the experiments. The results show that the SW-ViT outperforms several state-of-the-art methods in terms of classification accuracy. The proposed SW-ViT can be applied in daily clinical diagnosis.

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Gpmb-yolo: a lightweight model for efficient blood cell detection in medical imaging

Shi,

Zhu,

Zhou

et al. 2024

Health Inf Sci Syst

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TransMixNet: An Attention Based Double-Branch Model for White Blood Cell Classification and Its Training with the Fuzzified Training Data

Cited by 6 publications

References 16 publications

White blood cells segmentation and classification using a random forest and residual networks implementation

White blood cells segmentation and classification using a random forest and residual networks implementation

Shifted Window Vision Transformer for Blood Cell Classification

Gpmb-yolo: a lightweight model for efficient blood cell detection in medical imaging

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