Efficient leukocytes detection and classification in microscopic blood images using convolutional neural network coupled with a dual attention network

Khan, Siraj; Sajjad, Muhammad; Abbas, Naveed; Escorcia-Gutierrez, José; Gamarra, Margarita; Muhammad, Khan

doi:10.1016/j.compbiomed.2024.108146

Cited by 3 publications

(1 citation statement)

References 53 publications

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“…However, this method is primarily suited for handling multiple overlapping cells and cell clusters. Khan et al [18] combined a convolutional neural network with a dual attention network for the efficient detection and classification of white blood cells, adapting to various cell distribution scenarios. Machine learning and radiomics methods can extract and process the features of various blood cell attributes.…”

Section: Introductionmentioning

confidence: 99%

Blood Cell Attribute Classification Algorithm Based on Partial Label Learning

Feng,

Guo,

Luo

et al. 2024

Electronics

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Hematological morphology examinations, essential for diagnosing blood disorders, increasingly utilize deep learning. Blood cell classification, determined by combinations of cell attributes, is complicated by the complex relationships and subtle differences among the attributes, resulting in significant time and cost penalties. This study introduces the Partial Label Learning for Blood Cell Classification (P4BC) strategy, a method that trains neural networks using the blood cell attribute labeling data of weak annotations. Using morphological knowledge, we predefined candidate label sets for the blood cell attributes to blend this knowledge with deep learning. This improves the model’s prediction accuracy and interpretability in classifying attributes. This method effectively combines morphological knowledge with deep learning, an approach we refer to as knowledge alignment. It results in an 8.66% increase in attribute recognition accuracy and a 1.09% improvement in matching predictions to the candidate label sets, compared to the original method. These results confirm our method’s ability to grasp the characteristic information of blood cell attributes, enhancing the model interpretability and achieving knowledge alignment between hematological morphology and deep learning. Our algorithm ensures attribute classification accuracy and shows excellent cell category classification, highlighting its wide application potential and practical value in blood cell category classification.

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Section: Introductionmentioning

confidence: 99%

Blood Cell Attribute Classification Algorithm Based on Partial Label Learning

Feng,

Guo,

Luo

et al. 2024

Electronics

View full text Add to dashboard Cite

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CoTCoNet: An optimized coupled transformer-convolutional network with an adaptive graph reconstruction for leukemia detection

Raghaw,

Sharma,

Bansal

et al. 2024

Computers in Biology and Medicine

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An attention-based deep learning for acute lymphoblastic leukemia classification

Jawahar,

Anbarasi,

Narayanan

et al. 2024

Sci Rep

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The bone marrow overproduces immature cells in the malignancy known as Acute Lymphoblastic Leukemia (ALL). In the United States, about 6500 occurrences of ALL are diagnosed each year in both children and adults, comprising nearly 25% of pediatric cancer cases. Recently, many computer-assisted diagnosis (CAD) systems have been proposed to aid hematologists in reducing workload, providing correct results, and managing enormous volumes of data. Traditional CAD systems rely on hematologists’ expertise, specialized features, and subject knowledge. Utilizing early detection of ALL can aid radiologists and doctors in making medical decisions. In this study, Deep Dilated Residual Convolutional Neural Network (DDRNet) is presented for the classification of blood cell images, focusing on eosinophils, lymphocytes, monocytes, and neutrophils. To tackle challenges like vanishing gradients and enhance feature extraction, the model incorporates Deep Residual Dilated Blocks (DRDB) for faster convergence. Conventional residual blocks are strategically placed between layers to preserve original information and extract general feature maps. Global and Local Feature Enhancement Blocks (GLFEB) balance weak contributions from shallow layers for improved feature normalization. The global feature from the initial convolution layer, when combined with GLFEB-processed features, reinforces classification representations. The Tanh function introduces non-linearity. A Channel and Spatial Attention Block (CSAB) is integrated into the neural network to emphasize or minimize specific feature channels, while fully connected layers transform the data. The use of a sigmoid activation function concentrates on relevant features for multiclass lymphoblastic leukemia classification The model was analyzed with Kaggle dataset (16,249 images) categorized into four classes, with a training and testing ratio of 80:20. Experimental results showed that DRDB, GLFEB and CSAB blocks’ feature discrimination ability boosted the DDRNet model F1 score to 0.96 with minimal computational complexity and optimum classification accuracy of 99.86% and 91.98% for training and testing data. The DDRNet model stands out from existing methods due to its high testing accuracy of 91.98%, F1 score of 0.96, minimal computational complexity, and enhanced feature discrimination ability. The strategic combination of these blocks (DRDB, GLFEB, and CSAB) are designed to address specific challenges in the classification process, leading to improved discrimination of features crucial for accurate multi-class blood cell image identification. Their effective integration within the model contributes to the superior performance of DDRNet.

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Efficient leukocytes detection and classification in microscopic blood images using convolutional neural network coupled with a dual attention network

Cited by 3 publications

References 53 publications

Blood Cell Attribute Classification Algorithm Based on Partial Label Learning

Blood Cell Attribute Classification Algorithm Based on Partial Label Learning

CoTCoNet: An optimized coupled transformer-convolutional network with an adaptive graph reconstruction for leukemia detection

An attention-based deep learning for acute lymphoblastic leukemia classification

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