An ensemble-acute lymphoblastic leukemia model for acute lymphoblastic leukemia image classification

Mei-Ling Huang; Zong-Bin Huang; Mei-Ling Huang; Zong-Bin Huang

doi:10.3934/mbe.2024087

Mathematical Biosciences and Engineering

2024, Volume 21, Issue 2: 1959-1978. doi: 10.3934/mbe.2024087

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An ensemble-acute lymphoblastic leukemia model for acute lymphoblastic leukemia image classification

Mei-Ling Huang ^,,
Zong-Bin Huang

Department of Industrial Engineering & Management, National Chin-Yi University of Technology, Taichung, Taiwan

Academic Editor: Yang Kuang

Received: 27 October 2023 Revised: 15 December 2023 Accepted: 27 December 2023 Published: 05 January 2024

The timely diagnosis of acute lymphoblastic leukemia (ALL) is of paramount importance for enhancing the treatment efficacy and the survival rates of patients. In this study, we seek to introduce an ensemble-ALL model for the image classification of ALL, with the goal of enhancing early diagnostic capabilities and streamlining the diagnostic and treatment processes for medical practitioners. In this study, a publicly available dataset is partitioned into training, validation, and test sets. A diverse set of convolutional neural networks, including InceptionV3, EfficientNetB4, ResNet50, CONV_POOL-CNN, ALL-CNN, Network in Network, and AlexNet, are employed for training. The top-performing four individual models are meticulously chosen and integrated with the squeeze-and-excitation (SE) module. Furthermore, the two most effective SE-embedded models are harmoniously combined to create the proposed ensemble-ALL model. This model leverages the Bayesian optimization algorithm to enhance its performance. The proposed ensemble-ALL model attains remarkable accuracy, precision, recall, F1-score, and kappa scores, registering at 96.26, 96.26, 96.26, 96.25, and 91.36%, respectively. These results surpass the benchmarks set by state-of-the-art studies in the realm of ALL image classification. This model represents a valuable contribution to the field of medical image recognition, particularly in the diagnosis of acute lymphoblastic leukemia, and it offers the potential to enhance the efficiency and accuracy of medical professionals in the diagnostic and treatment processes.
- medical image classification,
- acute lymphoblastic leukemia,
- deep learning,
- convolutional neural networks
Citation: Mei-Ling Huang, Zong-Bin Huang. An ensemble-acute lymphoblastic leukemia model for acute lymphoblastic leukemia image classification[J]. Mathematical Biosciences and Engineering, 2024, 21(2): 1959-1978. doi: 10.3934/mbe.2024087

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Abstract

The timely diagnosis of acute lymphoblastic leukemia (ALL) is of paramount importance for enhancing the treatment efficacy and the survival rates of patients. In this study, we seek to introduce an ensemble-ALL model for the image classification of ALL, with the goal of enhancing early diagnostic capabilities and streamlining the diagnostic and treatment processes for medical practitioners. In this study, a publicly available dataset is partitioned into training, validation, and test sets. A diverse set of convolutional neural networks, including InceptionV3, EfficientNetB4, ResNet50, CONV_POOL-CNN, ALL-CNN, Network in Network, and AlexNet, are employed for training. The top-performing four individual models are meticulously chosen and integrated with the squeeze-and-excitation (SE) module. Furthermore, the two most effective SE-embedded models are harmoniously combined to create the proposed ensemble-ALL model. This model leverages the Bayesian optimization algorithm to enhance its performance. The proposed ensemble-ALL model attains remarkable accuracy, precision, recall, F1-score, and kappa scores, registering at 96.26, 96.26, 96.26, 96.25, and 91.36%, respectively. These results surpass the benchmarks set by state-of-the-art studies in the realm of ALL image classification. This model represents a valuable contribution to the field of medical image recognition, particularly in the diagnosis of acute lymphoblastic leukemia, and it offers the potential to enhance the efficiency and accuracy of medical professionals in the diagnostic and treatment processes.

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