Multi-modal image classification of COVID-19 cases using computed tomography and X-rays scans

Nasir, Nida; Kansal, Afreen; Barneih, Feras; Alshaltone, Omar; Bonny, Talal; Al-Shabi, Mohammad; Shammaa, Ahmed Al

doi:10.1016/j.iswa.2022.200160

Cited by 39 publications

(23 citation statements)

References 34 publications

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“…Besides that, our study found that Artificial Intelligence has performed very efficiently with existing or new models for detection of Covid. Thoes studies which are critically reviewed, they used deep learning algorithms for analysing image data, particularly only on X-rays and CT scans with exception [43], which used clinical notes along with image data. We have suggested five possible way to research in future based on these findings.…”

Section: Discussionmentioning

confidence: 99%

“…Other Data Should Include -In these articles we noticed that when researcher use CT scan and X-ray data usually they do not consider any other kind of data like health history, pathological, clinical etc. with exception of one Article [43]. If researcher include other related data for the research, the study will be strong and detection process will be more accurate.…”

Section: Future Research Opportunitiesmentioning

confidence: 99%

“…All of the studies use only image data as our searching key word was "X-ray" and "CT Scan". But there is only article by Nasir N. et al [43] which include clinical notes along with the X-ray and CT scan images. They found only 485 clinical notes available for 535 images.…”

Section: Exploration Of Used Datamentioning

confidence: 99%

“…Seven of them used only Kaggle dataset to collect their data for training or testing purpose of developed model [35,38,42,44,46,49,51]. Three of them used only GitHub repository which was developed by Dr. Joseph Cohen [33,43,48], other four of them used both of the Kaggle and GitHub database [34,47,50,54]. One used all of the GitHub, Covid-19 radiography database, Kaggle Covid-19 image data collection, and Actual Med Covid-19 Chest X-ray Dataset [36].…”

Section: Exploration Of Used Datamentioning

confidence: 99%

“…Data Source Data Volume [42] Kaggle 2482 CT scans image and 31 Covid positive along with 10,192 normal X-ray image [48] GitHub 108,948 images [49] Kaggle 3829 X-rays and 3829 X-rays [43] GitHub 535 CT and X-ray images and 485 clinical notes related with them [37] Mendeley Data 17,599 images [44] Kaggle 2481 images [46] Kaggle 2357 CT scan data, 2515 chest X-ray data and 2400 CT and chest X-ray hybrid data [34] Kaggle, GitHub 5856 chest X-ray & CT dataset [54] Kaggle, GitHub 2905 unique images for X-ray 617,775 images from 4154 patients [41] Mendeley Data 8,055 CT scan and 9,544 X-ray images [51] Kaggle 2591 mixed data [33] GitHub 17100 X−ray and CT images [47] Kaggle, GitHub 168 Covid and 168 normal cases for the both Xray and CT scan images [36] GitHub Kaggle and Git Hub 723 X-ray and 3228 CT scans images [38] Kaggle 400 chest X-ray images, and 400 CT scan images [39] Wonkwang University Hospital(WKUH) and Chonnam National University Hospital (CNUH), Italian Society of Medical and Interventional Radiology(SIRM) public database.…”

Section: Literaturementioning

confidence: 99%

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Comparative Analysis on Available Technique for the Detection of Covid-19 through CT-Scan and X-Ray using Machine Learning: A Systematic Review

Majhi¹,

Sauseng²

2023

Preprint

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(1) Background: In the year of 2020 Covid-19 was declared epidemic by WHO. From that time millions of people were affected and died by this disease. The main detection process for this epidemic is RT-PCR test or reverse polymerase transcription chain reaction test. One of the reason of spreading of this disease so much is lack of efficiency in RT-PCR test. Sampling error and low viral load were two main reasons for what the testing process faced such problems. Lung infection is a very common symptom for covid-19 patients, so, CT scan and chest X-ray imaging technique can be applied to detect patient at a very early stage of infection. Which will be very effective and also can be a better option to RT-PCR test; (2) Methods: We searched data in Scopus for articles published between 2020 and 2023. The initial set of articles was 189, from which 21 articles will eventually be selected by exclusion criteria; (3) Results: A total of thirteen (61.90%) articles were found working on detecting Covid-19 by extracting the X-ray and CT scan data individually. Three (14.28%) of those articles focused on a hybrid model of CT scan and X-ray Image Data. Another four articles made a comparison between Covid-19, pneumonia and a normal person to identify a Covid-19 patient. Where others have worked on unsupervised learning methods or SVM to detect Covid-19.; (4) Conclusions: We have conducted a systematic review of the studies that have been published up to this time, with the purpose to present a summary of the evidence about the detection of COVID-19. In this article, we have summarized and critically reviewed literatures which worked on development or application or both of different AI or ML technique from chest CT and X-ray images to find a solution to detect covid-19.

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Section: Discussionmentioning

confidence: 99%

Section: Future Research Opportunitiesmentioning

confidence: 99%

Section: Exploration Of Used Datamentioning

confidence: 99%

Section: Exploration Of Used Datamentioning

confidence: 99%

Section: Literaturementioning

confidence: 99%

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Comparative Analysis on Available Technique for the Detection of Covid-19 through CT-Scan and X-Ray using Machine Learning: A Systematic Review

Majhi¹,

Sauseng²

2023

Preprint

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Transfer Learning Fusion and Stacked Auto-encoders for Viral Lung Disease Classification

Ketfi,

Belahcene,

Bourennane

2024

New Gener. Comput.

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Dynamic modeling of photoacoustic sensor data to classify human blood samples

Pérez-Pacheco,

Ramírez-Chavarría,

Quispe-Siccha

et al. 2023

Med Biol Eng Comput

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The photoacoustic effect is an attractive tool for diagnosis in several biomedical applications. Analyzing photoacoustic signals, however, is challenging to provide qualitative results in an automated way. In this work, we introduce a dynamic modeling scheme of photoacoustic sensor data to classify blood samples according to their physiological status. Thirty-five whole human blood samples were studied with a state-space model estimated by a subspace method. Furthermore, the samples are classified using the model parameters and the linear discriminant analysis algorithm. The classification performance is compared with time- and frequency-domain features and an autoregressive-moving-average model. As a result, the proposed analysis can predict five blood classes: healthy women and men, microcytic and macrocytic anemia, and leukemia. Our findings indicate that the proposed method outperforms conventional signal processing techniques to analyze photoacoustic data for medical diagnosis. Hence, the method is a promising tool in point-of-care devices to detect hematological diseases in clinical scenarios. Graphical abstract

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Multi-modal image classification of COVID-19 cases using computed tomography and X-rays scans

Cited by 39 publications

References 34 publications

Comparative Analysis on Available Technique for the Detection of Covid-19 through CT-Scan and X-Ray using Machine Learning: A Systematic Review

Comparative Analysis on Available Technique for the Detection of Covid-19 through CT-Scan and X-Ray using Machine Learning: A Systematic Review

Transfer Learning Fusion and Stacked Auto-encoders for Viral Lung Disease Classification

Dynamic modeling of photoacoustic sensor data to classify human blood samples

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