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 6 publications

(1 citation statement)

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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%