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

被引：5

作者：

Huang M.-L. ^{[1
]}

Huang Z.-B. ^{[1
]}

机构：

[1] Department of Industrial Engineering & Management, National Chin-Yi University of Technology, Taichung

来源：

Mathematical Biosciences and Engineering | 2024年 / 21卷 / 02期

关键词：

acute lymphoblastic leukemia; convolutional neural networks; deep learning; medical image classification;

D O I：

10.3934/mbe.2024087

中图分类号：

学科分类号：

摘要：

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. ©2024 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License.

引用

页码：1959 / 1978

页数：19

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