Advanced leukocyte classification using attention mechanisms and dual channel U-Net architecture

TL;DR

This paper introduces a new deep learning model for accurately classifying white blood cells using advanced image processing and attention mechanisms.

Contribution

A novel Attention-based Dual Channel U-shaped Network (ADCU-Net) is proposed for improved leukocyte classification.

Findings

The ADCU-Net model achieved 98.4% accuracy in leukocyte classification.

Dung Beetle Optimization with Levy flight improved image segmentation accuracy.

Preprocessing steps significantly enhanced image clarity for better analysis.

Abstract

Leukocytes or white blood cells plays an important role in protecting the body from various contagious diseases and infectious agents. Different conventional leukocyte analysis approaches often face several problems like inaccuracies, demanding the need for sophisticated approaches to improve diagnostic precision. Therefore, a holistic structure namely a novel Attention-based Dual Channel U-shaped Network (ADCU-Net) utilizing three datasets is introduced in this paper for effective leukocyte classification. The image quality is boosted in the preprocessing phase through noise reduction, contrast enhancement, and background removal, significantly improving clarity. Then, the Dung Beetle Optimization (DBO) algorithm enhanced with Levy flight optimization is implemented for effective image segmentation processes. A dung beetle with a levy flight strategy assists in streamlined exploration of the search space thereby the detection and delineation of specific regions within images are improved, which results in higher boundary detection accuracy. The evaluation of major quantitative measures such as standard deviation, mean and entropy is comprised in the feature extraction process which offers crucial insights into the structural characteristics of leukocytes. Finally, a novel ADCU-Net model is utilized for the effective classification process. This ADCU-Net model is particularly selected to effectively capture various features and preserve spatial data, achieving98.4% accuracy. Overall, this paper highlights the performance of integrated sophisticated deep-learning structures for accurate leukocyte classification and segmentation, enabling the path for improved diagnostic tools in clinical settings.

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