EO-CNN: Equilibrium Optimization-Based hyperparameter tuning for enhanced pneumonia and COVID-19 detection using AlexNet and DarkNet19

IF 5.3 2区 医学 Q1 ENGINEERING, BIOMEDICAL Biocybernetics and Biomedical Engineering Pub Date : 2024-07-01 DOI:10.1016/j.bbe.2024.06.006
Soner Kiziloluk , Eser Sert , Mohamed Hammad , Ryszard Tadeusiewicz , Paweł Pławiak
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Abstract

Convolutional neural networks (CNN) have been increasingly popular in image categorization in recent years. Hyperparameter optimization is a critical stage in enhancing the effectiveness of CNNs and achieving better results. Properly tuning hyperparameters allows the model to exhibit improved performance and facilitates faster learning. Misconfigured hyperparameters can prolong the training time or lead to the model not learning at all. Manually tuning hyperparameters is a time-consuming and challenging process. Automatically adjusting hyperparameters helps save time and resources. This study aims to propose an approach that shows higher classification performance than unoptimized convolutional neural network models, even at low epoch values, by automatically optimizing the hyperparameters of AlexNet and DarkNet19 with equilibrium optimization, the newest metaheuristic algorithm. In this respect, the proposed approach optimizes the number and size of filters in the first five convolutional layers in AlexNet and DarkNet19 using an equilibrium optimization algorithm. To evaluate the efficacy of the suggested method, experimental analyses were conducted on the pneumonia and COVID-19 datasets. An important advantage of this approach is its ability to accurately classify medical images. The testing process suggests that utilizing the proposed approach to optimize hyperparameters for AlexNet and DarkNet19 led to a 7% and 4.07% improvement, respectively, in image classification accuracy compared to non-optimized versions of the same networks. Furthermore, the approach displayed superior classification performance even in a few epochs compared to AlexNet, ShuffleNet, DarkNet19, GoogleNet, MobileNet-V2, VGG-16, VGG-19, ResNet18, and Inceptionv3. As a result, automatic tuning of the hyperparameters of AlexNet and DarkNet-19 with EO enabled the performance of these two models to increase significantly.

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EO-CNN:基于均衡优化的超参数调整,利用 AlexNet 和 DarkNet19 增强肺炎和 COVID-19 检测能力
近年来,卷积神经网络(CNN)在图像分类领域越来越受欢迎。超参数优化是提高卷积神经网络效率并获得更好结果的关键阶段。适当调整超参数可以提高模型性能,加快学习速度。超参数配置不当会延长训练时间或导致模型根本无法学习。手动调整超参数是一个耗时且具有挑战性的过程。自动调整超参数有助于节省时间和资源。本研究旨在提出一种方法,通过使用最新的元启发式算法--均衡优化(equilibrium optimization)自动优化 AlexNet 和 DarkNet19 的超参数,与未优化的卷积神经网络模型相比,即使在较低的历时值下,也能显示出更高的分类性能。在这方面,所提出的方法利用平衡优化算法优化了 AlexNet 和 DarkNet19 前五个卷积层中过滤器的数量和大小。为了评估所建议方法的有效性,我们在肺炎和 COVID-19 数据集上进行了实验分析。这种方法的一个重要优势是能够准确地对医学图像进行分类。测试结果表明,利用建议的方法优化 AlexNet 和 DarkNet19 的超参数,与相同网络的非优化版本相比,图像分类准确率分别提高了 7% 和 4.07%。此外,与 AlexNet、ShuffleNet、DarkNet19、GoogleNet、MobileNet-V2、VGG-16、VGG-19、ResNet18 和 Inceptionv3 相比,该方法甚至在几个历时内就显示出了卓越的分类性能。因此,利用 EO 自动调整 AlexNet 和 DarkNet-19 的超参数可显著提高这两个模型的性能。
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来源期刊
CiteScore
16.50
自引率
6.20%
发文量
77
审稿时长
38 days
期刊介绍: Biocybernetics and Biomedical Engineering is a quarterly journal, founded in 1981, devoted to publishing the results of original, innovative and creative research investigations in the field of Biocybernetics and biomedical engineering, which bridges mathematical, physical, chemical and engineering methods and technology to analyse physiological processes in living organisms as well as to develop methods, devices and systems used in biology and medicine, mainly in medical diagnosis, monitoring systems and therapy. The Journal''s mission is to advance scientific discovery into new or improved standards of care, and promotion a wide-ranging exchange between science and its application to humans.
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