FWLICM-Deep Learning: Fuzzy Weighted Local Information C-Means Clustering-Based Lung Lobe Segmentation with Deep Learning for COVID-19 Detection.
COVID
Fuzzy local information c-means clustering
Random multimodel deep learning
Sine cosine algorithm
Water cycle algorithm
Journal
Journal of digital imaging
ISSN: 1618-727X
Titre abrégé: J Digit Imaging
Pays: United States
ID NLM: 9100529
Informations de publication
Date de publication:
12 2022
12 2022
Historique:
received:
14
09
2021
accepted:
06
06
2022
revised:
26
04
2022
pubmed:
6
7
2022
medline:
3
12
2022
entrez:
5
7
2022
Statut:
ppublish
Résumé
Coronavirus (COVID-19) creates an extensive range of respiratory contagions, and it is a kind of ribonucleic acid (RNA) virus, which affects both animals and humans. Moreover, COVID-19 is a new disease, which produces contamination in upper respiration alterritory and lungs. The new COVID is a rapidly spreading pathogen globally, and it threatens billions of humans' lives. However, it is significant to identify positive cases in order to avoid the spread of plague and to speedily treat infected patients. Hence, in this paper, the WSCA-based RMDL approach is devised for COVID-19 prediction by means of chest X-ray images. Moreover, Fuzzy Weighted Local Information C-Means (FWLICM) approach is devised in order to segment lung lobes. The developed FWLICM method is designed by modifying the Fuzzy Local Information C-Means (FLICM) technique. Additionally, random multimodel deep learning (RMDL) classifier is utilized for the COVID-19 prediction process. The new optimization approach, named water sine cosine algorithm (WSCA), is devised in order to obtain an effective prediction. The developed WSCA is newly designed by incorporating sine cosine algorithm (SCA) and water cycle algorithm (WCA). The developed WSCA-driven RMDL approach outperforms other COVID-19 prediction techniques with regard to accuracy, specificity, sensitivity, and dice score of 92.41%, 93.55%, 92.14%, and 90.02%.
Identifiants
pubmed: 35790588
doi: 10.1007/s10278-022-00667-y
pii: 10.1007/s10278-022-00667-y
pmc: PMC9255540
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1463-1478Informations de copyright
© 2022. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.
Références
Togacar, M., Ergen, B. and Cömert, Z., “COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches”, Computers in biology and medicine, vol.121, pp.103805, 2020.
doi: 10.1016/j.compbiomed.2020.103805
pubmed: 32568679
pmcid: 7202857
Rupapara V, Narra M, Gunda NK, Gandhi S, Thipparthy KR: Maintaining Social Distancing in Pandemic Using Smartphones With Acoustic Waves. IEEE Transact Computation Soc Syst 1–7,2021
Khan AI, Shah JL, Bhat MM: CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images. Comp Methods Prog Biomed 196:105581,2020
doi: 10.1016/j.cmpb.2020.105581
pubmed: 32534344
pmcid: 7274128
Ozturk T, Talo M, Yildirim EA, Baloglu UB, Yildirim O, Acharya UR: Automated detection of COVID-19 cases using deep neural networks with X-ray images. Comput Biol Med 121:103792,2020
doi: 10.1016/j.compbiomed.2020.103792
pubmed: 32568675
pmcid: 7187882
Mangal A, Kalia S, Rajgopal H, Rangarajan K, Namboodiri V, Banerjee S, Arora C: CovidAID: COVID-19 detection using chest X-ray. arXiv preprint https://arxiv.org/abs/2004.09803 . 2020
Dev K, Khowaja SA, Jaiswal A, Bist AS, Saini V, Bhatia S: “Triage of Potential COVID-19 Patients from Chest X-ray Images using Hierarchical Convolutional Networks. arXiv preprint https://arxiv.org/abs/2011.00618 . 2020.
Qiao Z, Bae A, Glass LM, Xiao C, Sun J: FLANNEL: Focal Loss Based Neural Network Ensemble for COVID-19 Detection. arXiv preprint https://arxiv.org/abs/2010.16039 , 2020.
Rothe C, Schunk M, Sothmann P, Bretzel G, Froeschl G, Wallrauch C, Zimmer T, Thiel V, Janke C, Guggemos W, Seilmaier M: Transmission of 2019-nCoV infection from an asymptomatic contact in Germany. New England J Med 382(10):970-971,2020.
doi: 10.1056/NEJMc2001468
pubmed: 32003551
Singhal T: A review of coronavirus disease-2019 (COVID-19). The Indian J Pedriatics 87(4):281-286,2020.
doi: 10.1007/s12098-020-03263-6
pubmed: 32166607
Lancet T: COVID-19: too little, too late?. Lancet (London, England) 395(10226):755, 2020.
doi: 10.1016/S0140-6736(20)30522-5
Razai MS, Doerholt K, Ladhani S, Oakeshott P: Coronavirus disease 2019 (covid-19): a guide for UK GPs. BMJ 368,2020.
Peng X, Xu X, Li Y, Cheng L, Zhou X, Ren B: Transmission routes of 2019-nCoV and controls in dental practice. Int J Oral Sci 12(1):1-6,2020.
doi: 10.1038/s41368-020-0075-9
Zu ZY, Jiang MD, Xu PP, Chen W, Ni QQ, Lu GM, Zhang LJ: Coronavirus disease 2019 (COVID-19): a perspective from China. Radiology 296(2):E15-E25,2020.
doi: 10.1148/radiol.2020200490
pubmed: 32083985
Fusini F, Bisicchia S, Bottegoni C, Gigante A, Zanchini F, Busilacchi A: Nutraceutical supplement in the management of tendinopathies: a systematic review. Muscles, Ligaments Tendons J 6(1):48-57,2016.
doi: 10.32098/mltj.01.2016.06
pubmed: 27331031
pmcid: 4915461
Catani O, Cautiero G, Sergio F, Cattolico A, Calafiore D, de Sire A, Zanchini F: Medial Displacement Calcaneal Osteotomy for Unilateral Adult Acquired Flatfoot: Effects of Minimally Invasive Surgery on Pain, Alignment, Functioning, and Quality of Life. The J Foot Ankle Surge 60(2):358-361,2021.
doi: 10.1053/j.jfas.2020.11.003
pubmed: 33472755
Abolfazl Zargari Khuzani, Morteza Heidari, and S. Ali Shariati, “COVID-Classifier: an automated machine learning model to assist in the diagnosis of COVID-19 infection in chest X-ray images,” Scientific Reports, vol. 11, 2021.
Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, Van Der Laak JA, Van Ginneken B, Sánchez CI: A survey on deep learning in medical image analysis. Med Image Anal 42:60-88,2017.
doi: 10.1016/j.media.2017.07.005
pubmed: 28778026
Ker J, Wang L, Rao J, Lim T: Deep learning applications in medical image analysis. IEE Access 6:9375-9389,2017.
doi: 10.1109/ACCESS.2017.2788044
Faust O, Hagiwara Y, Hong TJ, Lih OS, Acharya UR: Deep learning for healthcare applications based on physiological signals: A review. Comput Methods Programs Biomed 161:1-13,2018.
doi: 10.1016/j.cmpb.2018.04.005
pubmed: 29852952
Toğaçar M, Ergen B, Cömert Z: Application of breast cancer diagnosis based on a combination of convolutional neural networks, ridge regression and linear discriminant analysis using invasive breast cancer images processed with autoencoders. Med Hypotheses 135:109503
Liu X, Deng Z, Yang Y: Recent progress in semantic image segmentation. AI Intel Rev 52(2):1089-1106,2019.
doi: 10.1007/s10462-018-9641-3
Jaiswal AK, Tiwari P, Kumar S, Gupta D, Khanna A, Rodrigues JJ: Identifying pneumonia in chest X-rays: A deep learning approach. Measurement 145:511-518,2019.
doi: 10.1016/j.measurement.2019.05.076
Vinolin V: Breast Cancer Detection by Optimal Classification using GWO Algorithm. Multimedia Res 2(2)10-18,2019.
Ganeshan R: Skin Cancer Detection with Optimized Neural Network via Hybrid Algorithm. Multimedia Res 3(2):27-34,2020.
doi: 10.46253/j.mr.v3i2.a4
Tahamtan A, Ardebili A: Real-time RT-PCR in COVID-19 detection: issues affecting the results. Expe Rev Mole Diagnost 20(5):453-454,2020.
doi: 10.1080/14737159.2020.1757437
pubmed: 32297805
Ismael AM, Şengür A: Deep learning approaches for COVID-19 detection based on chest X-ray images. Exp Syst App 164,2021.
Hussain E, Hasan M, Rahman A, Lee I, Tamanna T, Parvez MZ: CoroDet: A deep learning based classification for COVID-19 detection using chest X-ray images. 142, 2021.
Purohit K, Kesarwani A, Kisku DR, Dalui M: Covid-19 detection on chest x-ray and ct scan images using multi-image augmented deep learning model. BioRxiv, 2020.
Ahmed S, Hossain T, Hoque OB, Sarker S, Rahman S, Shah FM: Automated COVID-19 Detection from Chest X-Ray Images: .A High Resolution Network (HRNet) Approach. medRxiv, 2020.
Krinidis S, Chatzis V: A robust fuzzy local information C-means clustering algorithm. IEEE Transact Image Process 19(5):1328-1337,2010.
doi: 10.1109/TIP.2010.2040763
pubmed: 20089475
Kowsari K, Heidarysafa M, Brown DE, Meimandi KJ, Barnes LE: Rmdl: Random multimodel deep learning for classification. In Proc 2nd Int Conf Inform Syst Data Mining 19–28,2018.
Eskandar H, Sadollah A, Bahreininejad A, Hamdi M: Water cycle algorithm–A novel metaheuristic optimization method for solving constrained engineering optimization problems. Comp Struct 110:151-166,2012.
doi: 10.1016/j.compstruc.2012.07.010
Mirjalili S: SCA: a sine cosine algorithm for solving optimization problems. Knowledge-based Syst 96:120-133,2016.
doi: 10.1016/j.knosys.2015.12.022
Ismael AM, Sengur A: Deep learning approaches for COVID-19 detection based on chest X-ray images. Expert Syst App 164:114054,2021.
doi: 10.1016/j.eswa.2020.114054
pubmed: 33013005
Pandit MK, Banday SA: SARS n-CoV2–19 detection from chest x-ray images using deep neural networks. Int J Pervasive Comput Commun 2020.
Autee P, Bagwe S, Shah V, Srivastava K: StackNet-DenVIS: a multi-layer perceptron stacked ensembling approach for COVID-19 detection using X-ray images: Phys Eng Sci Med 43(4):1399-1414, 2020.
doi: 10.1007/s13246-020-00952-6
pubmed: 33275187
pmcid: 7715648
Zhang R, Guo Z, Sun Y, Lu Q, Xu Z, Yao Z, Duan M, Liu S, Ren Y, Huang L, Zhou F: COVID19XrayNet: A Two-Step Transfer Learning Model for the COVID-19 Detecting Problem Based on a Limited Number of Chest X-Ray Images. Interdis Sci Computl Life Sci 12(4):555-565,2020.
pubmed: 32959234
Bassi PR, Attux R: A deep convolutional neural network for covid-19 detection using chest x-rays. arXiv preprint https://arxiv.org/abs/2005.01578 . 2020.
DeepCovid Dataset: https://github.com/shervinmin/DeepCovid . Accessed on February 2021.
Hassan A, Shahin I, Bader M: COVID-19 Detection System Using Recurrent Neural Networks. In the Proc Int Conf Commun, Comput, Cybersec Informat 2020