Boosted crow search algorithm for handling multi-threshold image problems with application to X-ray images of COVID-19

Zhao, Songwei; Wang, Pengjun; Heidari, Ali Asghar; Zhao, Xuehua; Chen, Huiling

doi:10.1016/j.eswa.2022.119095

Cited by 26 publications

(3 citation statements)

References 146 publications

(151 reference statements)

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“…A few traditional optimization-based methods have been proposed for COVID-19 lesion segmentation [30] , [31] , [32] , [33] , [34] , which promote the studies of COVID-19 disease detection and treatment. For example, Qi et al.…”

Section: Related Workmentioning

confidence: 99%

A convolutional neural network with pixel-wise sparse graph reasoning for COVID-19 lesion segmentation in CT images

Tang

Cai

et al. 2023

Computers in Biology and Medicine

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Section: Related Workmentioning

confidence: 99%

A convolutional neural network with pixel-wise sparse graph reasoning for COVID-19 lesion segmentation in CT images

Tang

Cai

et al. 2023

Computers in Biology and Medicine

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“…The simplicity and effectiveness of this approach led to its selection. In order to maximize both exploitation and exploration, the suggested method incorporates the first enhancement type of pivoting (local searching) and the crow heuristic algorithm [9,10].…”

Section: Introductionmentioning

confidence: 99%

Security-Aware Resource Allocation in Fog Computing Environments

Kumar

2024

Preprint

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Fog computing, an innovative approach building upon cloud technology, taps into local user resources to enhance services. It boasts cost-effectiveness, improved security, and reduced network delays, making it a popular choice for real-time applications. However, due to the heterogeneity of fog devices, resource allocation and scheduling pose challenges. This paper introduces a novel solution using a crow-inspired search mechanism and a multi-objective evolutionary approach for fog computing environments. The primary objectives are to optimize security and success rates simultaneously. A local search method enhances the performance of the crow search algorithm (CSA). The proposed method utilizes evolutionary techniques to allocate and schedule resources effectively. We compared our hybrid CSA with the original CSA and Genetic Algorithm (GA) across seven scenarios with varying parameters. Our hybrid CSA consistently outperformed existing methods, demonstrating its effectiveness in achieving the desired objectives.

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“…In this study, it was found that most of the researchers have applied thresholding and clustering in radiographs segmentation. For the threshold-based segmentation, it can be summarized as two-level and multi-level thresholding while the multi-level thresholding can perform better in complex images compared to two-level threshold [10]. Mahdy L et al, [11] proposed a multi-level thresholding in image segmentation with the help of 40 CXR images and archived the average sensitivity, specificity and accuracy of 95.76%, 99.7% and 97.48% respectively in image classification.…”

Section: Introductionmentioning

confidence: 99%

Robust Segmentation of COVID-19 Chest X-Ray Images: Analysis of Variant k-Means Based Clustering Algorithms

Ooi Wei Herng,

Aimi Salihah Abdul Nasir,

Abdul Syafiq Abdull Sukor

2024

ARASET

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Computer aided diagnosis (CADx) become one the most famous method in diagnostic medical field due to the high reliability and efficiency. Recently, the coronavirus disease (COVID-19) has become severe global pandemic. Particularly, the Chest X-ray (CXR) imaging has become an essentiality in COVID-19 detection. As a result, the convergence of CADx technology with Chest X-ray analysis has achieved great efficiency in COVID-19 diagnosis. Therefore, the research value of CADx in COVID-19 diagnosis is exceptionally high. This study aims to evaluate different k-means based clustering algorithms and identifying the one with the highest overall accuracy. First of all, 150 COVID-19 CXR open-source images are acquired from Kaggle and Github. All the images will be unified into a same image size with 1000*1000 pixels and quality during the image pre-processing. Next, the resized images are enhanced by the Modified Global Contrast Stretching (MGCS) enhancement method to increase the quality of images. Then, the traditional k-means, k-medians, k-medoids and fast k-means clustering methods have been implemented in the image segmentation. At the same time, five different numbers <2, 4, 6, 8, 10> of clusters also tested out in this study. Lastly, all the segmented is proceeded to the segmentation performance based on sensitivity, specificity, accuracy, precision, recall and F-score. The result proves that the k-medoids clustering algorithm with 2 clusters archived the best overall segmentation performance as it obtained the highest sensitivity, accuracy, recall and F-score with 66.14%, 87.98%, 0.6614 and 0.7327.

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Boosted crow search algorithm for handling multi-threshold image problems with application to X-ray images of COVID-19

Cited by 26 publications

References 146 publications

A convolutional neural network with pixel-wise sparse graph reasoning for COVID-19 lesion segmentation in CT images

A convolutional neural network with pixel-wise sparse graph reasoning for COVID-19 lesion segmentation in CT images

Security-Aware Resource Allocation in Fog Computing Environments

Robust Segmentation of COVID-19 Chest X-Ray Images: Analysis of Variant k-Means Based Clustering Algorithms

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