AFCM-LSMA: New intelligent model based on Lévy slime mould algorithm and adaptive fuzzy C-means for identification of COVID-19 infection from chest X-ray images

Anter, Ahmed M.; Oliva, Diego; Thakare, Anuradha; Zhang, Zhiguo

doi:10.1016/j.aei.2021.101317

Cited by 48 publications

(10 citation statements)

References 37 publications

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“…Golden standard segmented GGO by doctor (GSSD), improved random walk combined with traditional FCM (IRWF) [29] , watershed image segmentation (WIS) [8] , automatic random walk image segmentation (ARW) [30] , traditional random walk image segmentation (TRWS) [31] , region growth combined with morphological (RGCM) [32] , super-pixel random walk image segmentation (SRWS) [33] , adaptive spatial information MRF combined with FCM and improved random walk algorithm (AMFRW, paper method) are used for simulation comparison tests.…”

Section: Analysis Of Experimental Results and Performancementioning

confidence: 99%

COVID-19 ground-glass opacity segmentation based on fuzzy c-means clustering and improved random walk algorithm

Wang

Guo

Han

et al. 2023

Biomedical Signal Processing and Control

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Section: Analysis Of Experimental Results and Performancementioning

confidence: 99%

COVID-19 ground-glass opacity segmentation based on fuzzy c-means clustering and improved random walk algorithm

Wang

Guo

Han

et al. 2023

Biomedical Signal Processing and Control

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“…Generally, for pulling out useful information, edge detection is useful which gives us the detailed information of the object. In the proposed model, Sobel Edge Detection algorithm 46 , 47 , 48 is used which is made up of 3*3 convolutional kernels.…”

Section: Proposed Modelmentioning

confidence: 99%

A deep learning approach for classification of COVID and pneumonia using DenseNet‐201

Sanghvi

Patel

Agarwal

et al. 2022

Int J Imaging Syst Tech

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In the present paper, our model consists of deep learning approach: DenseNet201 for detection of COVID and Pneumonia using the Chest X‐ray Images. The model is a framework consisting of the modeling software which assists in Health Insurance Portability and Accountability Act Compliance which protects and secures the Protected Health Information . The need of the proposed framework in medical facilities shall give the feedback to the radiologist for detecting COVID and pneumonia though the transfer learning methods. A Graphical User Interface tool allows the technician to upload the chest X‐ray Image. The software then uploads chest X‐ray radiograph (CXR) to the developed detection model for the detection. Once the radiographs are processed, the radiologist shall receive the Classification of the disease which further aids them to verify the similar CXR Images and draw the conclusion. Our model consists of the dataset from Kaggle and if we observe the results, we get an accuracy of 99.1%, sensitivity of 98.5%, and specificity of 98.95%. The proposed Bio‐Medical Innovation is a user‐ready framework which assists the medical providers in providing the patients with the best‐suited medication regimen by looking into the previous CXR Images and confirming the results. There is a motivation to design more such applications for Medical Image Analysis in the future to serve the community and improve the patient care.

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“…More the data is near the cluster centre, more is its membership towards the particular cluster centre. A development of an intelligent model based on the Lévy slime mould algorithm and adaptive fuzzy C-means for detecting COVID-19 infection in chest X-rays has been developed in (Anter et al, 2021). ROI extraction in CT lung images of COVID-19 using Fast Fuzzy C means clustering was discussed in (S. N. Kumar et al, 2021).…”

Section: Unsupervised Learningmentioning

confidence: 99%

Battling COVID-19 using machine learning: A review

Chadaga

Prabhu

et al. 2021

Cogent Engineering

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Severe Acute Respiratory Syndrome Coronavirus 2(SARS-CoV-2) known as Coronavirus surfaced in late 2019. It turned out to be a life-threatening disease and is causing chaos all around the world. The World Health Organisation (WHO) declared it a pandemic in March 2020. To handle COVID-19 related problems, research in many areas of science was introduced. Machine learning (ML), being one of the most successful stories in recent times is widely used to solve a variety of problems in our everyday life. Here, an overview of machine learning that tackles the pandemic is discussed in the beginning. Various datasets related to COVID-19 are also explored. Diagnosis of this viral disease using CT-Scans, X-ray images, sound analysis and blood tests using machine learning are presented in-depth. Drug and vaccine development using machine learning for COVID-19 are also discussed. Pandemic management and control were also examined. The main objective of this paper is to conduct a systematic review of machine learning applications that fight the deadly virus. This paper helps the researchers to understand and analyse the data trends related to COVID-19 and also

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AFCM-LSMA: New intelligent model based on Lévy slime mould algorithm and adaptive fuzzy C-means for identification of COVID-19 infection from chest X-ray images

Cited by 48 publications

References 37 publications

COVID-19 ground-glass opacity segmentation based on fuzzy c-means clustering and improved random walk algorithm

COVID-19 ground-glass opacity segmentation based on fuzzy c-means clustering and improved random walk algorithm

A deep learning approach for classification of COVID and pneumonia using DenseNet‐201

Battling COVID-19 using machine learning: A review

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