W-Net: A CNN-based Architecture for White Blood Cells Image Classification

Jung, Changhun; Abuhamad, Mohammed; Alikhanov, Jumabek; Mohaisen, Aziz; Han, Ke; Nyang, DaeHun

doi:10.48550/arxiv.1910.01091

Cited by 7 publications

(7 citation statements)

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“…The results showed that the classification performance was higher when the images were preprocessed with these filters compared to the original data. Jung et al [38] proposed W-Net, a CNN-based method for the classification of WBCs. To evaluate W-Net, they used a large-scale dataset of 6,562 real images of the five WBCs types, obtained from The Catholic University of Korea.…”

Section: Discussionmentioning

confidence: 99%

Automatic Classification of White Blood Cells Using Pre-Trained Deep Models

KATAR¹,

Fırat²

2022

Sakarya University Journal of Computer and Information Sciences

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White blood cells (WBCs), which are part of the immune system, help our body fight infections and other diseases. Certain diseases can cause our body to produce fewer WBCs than it needs. For this reason, WBCs are of great importance in the field of medical imaging. Artificial intelligence-based computer systems can assist experts in the analysis of WBCs. In this study, an approach is proposed for the automatic classification of WBCs over five different classes using a pre-trained model. ResNet-50, VGG-19, and MobileNet-V3-Small pre-trained models were trained with ImageNet weights. In the training, validation, and testing processes of the models, a public dataset containing 16,633 images and not having an even class distribution was used. While the ResNet-50 model reached 98.79% accuracy, the VGG-19 model reached 98.19% accuracy, the MobileNet-V3-Small model reached the highest accuracy rate with 98.86%. When the predictions of the MobileNet-V3-Small model are examined, it is seen that it is not affected by class dominance and can classify even the least sampled class images in the dataset correctly. WBCs were classified with high accuracy using the proposed pre-trained deep learning models. Experts can effectively use the proposed approach in the process of analyzing WBCs.

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Section: Discussionmentioning

confidence: 99%

Automatic Classification of White Blood Cells Using Pre-Trained Deep Models

KATAR¹,

Fırat²

2022

Sakarya University Journal of Computer and Information Sciences

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“…Jung et al [13] introduced W-Net, a CNN-based model for white blood cell (WBC) classification, achieving 97% accuracy with 10-fold cross-validation. Their work focused on classifying five WBC types: neutrophils, eosinophils, basophils, lymphocytes, and monocytes.…”

Section: Related Workmentioning

confidence: 99%

Blood Cell Revolution: Unveiling 11 Distinct Types with ‘Naturalize’ Augmentation

Abou Ali,

Dornaika,

Arganda-Carreras

2023

Algorithms

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Artificial intelligence (AI) has emerged as a cutting-edge tool, simultaneously accelerating, securing, and enhancing the diagnosis and treatment of patients. An exemplification of this capability is evident in the analysis of peripheral blood smears (PBS). In university medical centers, hematologists routinely examine hundreds of PBS slides daily to validate or correct outcomes produced by advanced hematology analyzers assessing samples from potentially problematic patients. This process may logically lead to erroneous PBC readings, posing risks to patient health. AI functions as a transformative tool, significantly improving the accuracy and precision of readings and diagnoses. This study reshapes the parameters of blood cell classification, harnessing the capabilities of AI and broadening the scope from 5 to 11 specific blood cell categories with the challenging 11-class PBC dataset. This transformation facilitates a more profound exploration of blood cell diversity, surpassing prior constraints in medical image analysis. Our approach combines state-of-the-art deep learning techniques, including pre-trained ConvNets, ViTb16 models, and custom CNN architectures. We employ transfer learning, fine-tuning, and ensemble strategies, such as CBAM and Averaging ensembles, to achieve unprecedented accuracy and interpretability. Our fully fine-tuned EfficientNetV2 B0 model sets a new standard, with a macro-average precision, recall, and F1-score of 91%, 90%, and 90%, respectively, and an average accuracy of 93%. This breakthrough underscores the transformative potential of 11-class blood cell classification for more precise medical diagnoses. Moreover, our groundbreaking “Naturalize” augmentation technique produces remarkable results. The 2K-PBC dataset generated with “Naturalize” boasts a macro-average precision, recall, and F1-score of 97%, along with an average accuracy of 96% when leveraging the fully fine-tuned EfficientNetV2 B0 model. This innovation not only elevates classification performance but also addresses data scarcity and bias in medical deep learning. Our research marks a paradigm shift in blood cell classification, enabling more nuanced and insightful medical analyses. The “Naturalize” technique’s impact extends beyond blood cell classification, emphasizing the vital role of diverse and comprehensive datasets in advancing healthcare applications through deep learning.

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“…Numerous research studies and publications have focused on the autonomous image analysis of white blood cells in microscopic peripheral blood smears. These studies leveraged transfer-based learning from pre-trained ImageNet models across various dataset sizes [7][8][9][10][11][12][13][14].…”

Section: Related Workmentioning

confidence: 99%

“…Jung et al [10] introduced "W-Net", a CNN-based architecture for classifying five different types of white blood cells. They utilized a dataset from the Catholic University of Korea (CUK), comprising 6562 images of these five WBC types.…”

Section: Related Workmentioning

confidence: 99%

White Blood Cell Classification: Convolutional Neural Network (CNN) and Vision Transformer (ViT) under Medical Microscope

Abou Ali,

Dornaika,

Arganda-Carreras

2023

Algorithms

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Deep learning (DL) has made significant advances in computer vision with the advent of vision transformers (ViTs). Unlike convolutional neural networks (CNNs), ViTs use self-attention to extract both local and global features from image data, and then apply residual connections to feed these features directly into a fully networked multilayer perceptron head. In hospitals, hematologists prepare peripheral blood smears (PBSs) and read them under a medical microscope to detect abnormalities in blood counts such as leukemia. However, this task is time-consuming and prone to human error. This study investigated the transfer learning process of the Google ViT and ImageNet CNNs to automate the reading of PBSs. The study used two online PBS datasets, PBC and BCCD, and transferred them into balanced datasets to investigate the influence of data amount and noise immunity on both neural networks. The PBC results showed that the Google ViT is an excellent DL neural solution for data scarcity. The BCCD results showed that the Google ViT is superior to ImageNet CNNs in dealing with unclean, noisy image data because it is able to extract both global and local features and use residual connections, despite the additional time and computational overhead.

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W-Net: A CNN-based Architecture for White Blood Cells Image Classification

Cited by 7 publications

References 0 publications

Automatic Classification of White Blood Cells Using Pre-Trained Deep Models

Automatic Classification of White Blood Cells Using Pre-Trained Deep Models

Blood Cell Revolution: Unveiling 11 Distinct Types with ‘Naturalize’ Augmentation

White Blood Cell Classification: Convolutional Neural Network (CNN) and Vision Transformer (ViT) under Medical Microscope

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