White Blood Cell Classification Using Multi-Attention Data Augmentation and Regularization

Bayat, Nasrin; Davey, Diane D.; Coathup, Melanie; Park, Joon‐Hyuk

doi:10.3390/bdcc6040122

Cited by 11 publications

(4 citation statements)

References 46 publications

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“…Attenuating frequencies using low pass Gaussian filters [31] results in a smoother image in the spatial domain. This process is formalized in the following equations:…”

Section: B Notation and Problem Formulationmentioning

confidence: 99%

Improving Performance of Object Detection using the Mechanisms of Visual Recognition in Humans

Ghasemi¹,

Mottaghian²,

Bayat³

2023

Preprint

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Object recognition systems are usually trained and evaluated on high resolution images. However, in real world applications, it is common that the images have low resolutions or have small sizes. In this study, we first track the performance of the state-of-the-art deep object recognition network, Faster-RCNN, as a function of image resolution. The results reveals negative effects of low resolution images on recognition performance. They also show that different spatial frequencies convey different information about the objects in recognition process. It means multi-resolution recognition system can provides better insight into optimal selection of features that results in better recognition of objects. This is similar to the mechanisms of the human visual systems that are able to implement multi-scale representation of a visual scene simultaneously. Then, we propose a multi-resolution object recognition framework rather than a single-resolution network. The proposed framework is evaluated on the PASCAL VOC2007 database. The experimental results show the performance of our adapted multi-resolution Faster-RCNN framework outperforms the single-resolution Faster-RCNN on input images with various resolutions with an increase in the mean Average Precision (mAP) of 9.14% across all resolutions and 1.2% on the full-spectrum images. Furthermore, the proposed model yields robustness of the performance over a wide range of spatial frequencies.

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“…Attenuating frequencies using low pass Gaussian filters [31] results in a smoother image in the spatial domain. This process is formalized in the following equations:…”

Section: B Notation and Problem Formulationmentioning

confidence: 99%

Improving Performance of Object Detection using the Mechanisms of Visual Recognition in Humans

Ghasemi¹,

Mottaghian²,

Bayat³

2023

Preprint

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show abstract

“…The first CNN classified the samples as normal white blood cells or abnormal ones, while the second CNN classified the abnormal samples into eight different subtypes. Bayat, et al [24] designed an attention-based CNN with regularization techniques to classify white blood cells. The model could fuse texture features with global features from the blood cell images.…”

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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“…However, there are still some deficiencies in these models. Some of them would use handcrafted features [24][25][26][27], but these features could not be the ideal maps for blood cell diagnosis. Meanwhile, DL models could take a lot of time to complete the experiments because of the massive layers and parameters.…”

Section: Introductionmentioning

confidence: 99%

DLBCNet: A Deep Learning Network for Classifying Blood Cells

Zhu

Ren

et al. 2023

BDCC

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Background: Blood is responsible for delivering nutrients to various organs, which store important health information about the human body. Therefore, the diagnosis of blood can indirectly help doctors judge a person’s physical state. Recently, researchers have applied deep learning (DL) to the automatic analysis of blood cells. However, there are still some deficiencies in these models. Methods: To cope with these issues, we propose a novel network for the multi-classification of blood cells, which is called DLBCNet. A new specifical model for blood cells (BCGAN) is designed to generate synthetic images. The pre-trained ResNet50 is implemented as the backbone model, which serves as the feature extractor. The extracted features are fed to the proposed ETRN to improve the multi-classification performance of blood cells. Results: The average accuracy, average sensitivity, average precision, average specificity, and average f1-score of the proposed model are 95.05%, 93.25%, 97.75%, 93.72%, and 95.38%, accordingly. Conclusions: The performance of the proposed model surpasses other state-of-the-art methods in reported classification results.

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White Blood Cell Classification Using Multi-Attention Data Augmentation and Regularization

Cited by 11 publications

References 46 publications

Improving Performance of Object Detection using the Mechanisms of Visual Recognition in Humans

Improving Performance of Object Detection using the Mechanisms of Visual Recognition in Humans

Shifted Window Vision Transformer for Blood Cell Classification

DLBCNet: A Deep Learning Network for Classifying Blood Cells

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