COVID-19 detection using chest X-ray images based on a developed deep neural network

Mousavi, Zohreh; Shahini, Nahal; Sheykhivand, Sobhan; Mojtahedi, Sina; Arshadi, Afrooz

doi:10.1016/j.slast.2021.10.011

Cited by 42 publications

(11 citation statements)

References 42 publications

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“…Long short-term memory (LSTM) is one typical application of RNN networks to detect the object in an image and shows great potential in medical diagnosing [ 34 – 36 ]. Mousavi et al [ 37 ] combine CNN and LSTM and make an analysis based on seven binary categorical classifications among COVID-19, viral pneumonia, bacterial pneumonia, and healthy people.…”

Section: Related Workmentioning

confidence: 99%

PneuNet: deep learning for COVID-19 pneumonia diagnosis on chest X-ray image analysis using Vision Transformer

Wang

Nie

Wang

et al. 2023

Med Biol Eng Comput

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A long-standing challenge in pneumonia diagnosis is recognizing the pathological lung texture, especially the ground-glass appearance pathological texture. One main difficulty lies in precisely extracting and recognizing the pathological features. The patients, especially those with mild symptoms, show very little difference in lung texture, neither conventional computer vision methods nor convolutional neural networks perform well on pneumonia diagnosis based on chest X-ray (CXR) images. In the meanwhile, the Coronavirus Disease 2019 (COVID-19) pandemic continues wreaking havoc around the world, where quick and accurate diagnosis backed by CXR images is in high demand. Rather than simply recognizing the patterns, extracting feature maps from the original CXR image is what we need in the classification process. Thus, we propose a Vision Transformer (VIT)–based model called PneuNet to make an accurate diagnosis backed by channel-based attention through X-ray images of the lung, where multi-head attention is applied on channel patches rather than feature patches. The techniques presented in this paper are oriented toward the medical application of deep neural networks and VIT. Extensive experiment results show that our method can reach 94.96% accuracy in the three-categories classification problem on the test set, which outperforms previous deep learning models.

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Section: Related Workmentioning

confidence: 99%

PneuNet: deep learning for COVID-19 pneumonia diagnosis on chest X-ray image analysis using Vision Transformer

Wang

Nie

Wang

et al. 2023

Med Biol Eng Comput

View full text Add to dashboard Cite

show abstract

“…They also proposed a novel U-Net model with gamma correlation-based enhancement performing the best. Mousavi et al [ 33 ] presented a CNN-LSTM-based network testing on six different databases and separated COVID-19 infected scans from healthy ones and other lung diseases with more than 90 % accuracy. Distinctive filters could also be utilized to study different kinds of pneumonia.…”

Section: Related Workmentioning

confidence: 99%

Conv-CapsNet: capsule based network for COVID-19 detection through X-Ray scans

Sharma

Arya

Verma

et al. 2023

Multimed Tools Appl

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“…As of now, professionals from all across the world are working hard to fight against the disease. Many researchers and academicians published different articles describing methods for detecting COVID-19 using CXR images [18] , [19] , [20] , [21] , [22] . Using image processing on chest X-ray images, Hasoon et al suggested a method for classifying and early detecting COVID-19.…”

Section: Introductionmentioning

confidence: 99%

SCovNet: A skip connection-based feature union deep learning technique with statistical approach analysis for the detection of COVID-19

Patro

Prakash

Hammad

et al. 2023

Biocybernetics and Biomedical Engineering

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COVID-19 detection using chest X-ray images based on a developed deep neural network

Cited by 42 publications

References 42 publications

PneuNet: deep learning for COVID-19 pneumonia diagnosis on chest X-ray image analysis using Vision Transformer

PneuNet: deep learning for COVID-19 pneumonia diagnosis on chest X-ray image analysis using Vision Transformer

Conv-CapsNet: capsule based network for COVID-19 detection through X-Ray scans

SCovNet: A skip connection-based feature union deep learning technique with statistical approach analysis for the detection of COVID-19

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