Performance optimization of salp swarm algorithm for multi-threshold image segmentation: Comprehensive study of breast cancer microscopy

Zhao, Songwei; Wang, Pengjun; Heidari, Ali Asghar; Chen, Huiling; He, Wenming; Xu, Suling

doi:10.1016/j.compbiomed.2021.105015

Cited by 58 publications

(20 citation statements)

References 118 publications

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“…Image segmentation has been applied to solve many practical problems, such as leaf spot disease image segmentation [ 40 ], breast cancer image segmentation [ 41 , 42 ], and lupus nephritis image segmentation. Today, many COVID-19 X-ray image segmentation methods have been proposed by researchers.…”

Section: Related Workmentioning

confidence: 99%

Directional mutation and crossover boosted ant colony optimization with application to COVID-19 X-ray image segmentation

Zhao

et al. 2022

Computers in Biology and Medicine

Self Cite

160

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Section: Related Workmentioning

confidence: 99%

Directional mutation and crossover boosted ant colony optimization with application to COVID-19 X-ray image segmentation

Zhao

et al. 2022

Computers in Biology and Medicine

Self Cite

160

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“…There are many methods to process images efficiently in the computer field [ [7] , [8] , [9] ], among which image segmentation is an integral part of image processing and a hot topic of research nowadays [ [10] , [11] , [12] ]. In the past few years, more and more image segmentation techniques have been proposed, such as multilevel threshold image segmentation (MTIS) [ 13 ], deep learning-based image segmentation [ 14 ], hierarchical clustering-based image segmentation [ 15 ], wavelet transform-based image segmentation [ 16 ], and others.…”

Section: Introductionmentioning

confidence: 99%

Multilevel threshold image segmentation for COVID-19 chest radiography: A framework using horizontal and vertical multiverse optimization

Zhao

Elmannai

et al. 2022

Computers in Biology and Medicine

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129

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“…Several imaging modalities are used to diagnose BC, such as mammography, breast ultrasound, MRI, and computed tomography (CT). Furthermore, microscopic images are also used to find breast cancer [ 2 , 3 ]. However, mammography is presently one of the recommended diagnostic procedures to detect early-stage breast cancer [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Cloud Computing-Based Framework for Breast Tumor Image Classification Using Fusion of AlexNet and GLCM Texture Features with Ensemble Multi-Kernel Support Vector Machine (MK-SVM)

Alyami

Sadad

Rehman

et al. 2022

Computational Intelligence and Neuroscience

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Breast cancer is common among women all over the world. Early identification of breast cancer lowers death rates. However, it is difficult to determine whether these are cancerous or noncancerous lesions due to their inconsistencies in image appearance. Machine learning techniques are widely employed in imaging analysis as a diagnostic method for breast cancer classification. However, patients cannot take advantage of remote areas as these systems are unavailable on clouds. Thus, breast cancer detection for remote patients is indispensable, which can only be possible through cloud computing. The user is allowed to feed images into the cloud system, which is further investigated through the computer aided diagnosis (CAD) system. Such systems could also be used to track patients, older adults, especially with disabilities, particularly in remote areas of developing countries that do not have medical facilities and paramedic staff. In the proposed CAD system, a fusion of AlexNet architecture and GLCM (gray-level cooccurrence matrix) features are used to extract distinguishable texture features from breast tissues. Finally, to attain higher precision, an ensemble of MK-SVM is used. For testing purposes, the proposed model is applied to the MIAS dataset, a commonly used breast image database, and achieved 96.26% accuracy.

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Performance optimization of salp swarm algorithm for multi-threshold image segmentation: Comprehensive study of breast cancer microscopy

Cited by 58 publications

References 118 publications

Directional mutation and crossover boosted ant colony optimization with application to COVID-19 X-ray image segmentation

Directional mutation and crossover boosted ant colony optimization with application to COVID-19 X-ray image segmentation

Multilevel threshold image segmentation for COVID-19 chest radiography: A framework using horizontal and vertical multiverse optimization

Cloud Computing-Based Framework for Breast Tumor Image Classification Using Fusion of AlexNet and GLCM Texture Features with Ensemble Multi-Kernel Support Vector Machine (MK-SVM)

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