FBSED based automatic diagnosis of COVID-19 using X-ray and CT images

Chaudhary, Pradeep Kumar; Pachori, Ram Bilas

doi:10.1016/j.compbiomed.2021.104454

Cited by 66 publications

(30 citation statements)

References 32 publications

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“…Recent ML-based studies related to COVID-19 diagnosis mostly use X-ray or CT scan ( [49], [50], [51], [52]). Although they provide high classification accuracies, they oblige people to go to hospitals for screening.…”

Section: Discussionmentioning

confidence: 99%

A transfer learning-based deep learning approach for automated COVID-19 diagnosis with audio data

Akgün¹,

Kabakuş²,

Şentürk³

et al. 2021

Turk J Elec Eng & Comp Sci

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The COVID-19 pandemic has caused millions of deaths and changed daily life globally. Countries have declared a half or full lockdown to prevent the spread of COVID-19. According to medical doctors, as many people as possible should be tested to identify their status, and corresponding actions then should be taken for COVID-19 positive cases. Despite the clear necessity of these medical tests, many countries are still struggling to acquire them. This fact clearly indicates the necessity of a large-scale, cheap, fast, and accurate alternative pre-screening tool that can be used for the diagnosis of COVID-19 while waiting for the medical tests. To this end, a novel end-to-end transfer learning-based deep learning approach that uses only a given cough sound for the diagnosis of COVID-19 was proposed in this study.The proposed models employed various pre-trained deep neural networks, namely, VGG19, ResNet50V2, DenseNet121, and MobileNet, via the transfer-learning technique. Then, these models were evaluated on a gold standard dataset, namely, Cambridge Data. According to the experimental result, the proposed model, which employed the MobileNet via the transfer-learning technique, provided the best accuracy, 86.42 %, and outperformed the state-of-the-art. Thus, the proposed model has the potential to provide automated COVID-19 diagnosis in an easily applicable and fast yet accurate way.

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Section: Discussionmentioning

confidence: 99%

A transfer learning-based deep learning approach for automated COVID-19 diagnosis with audio data

Akgün¹,

Kabakuş²,

Şentürk³

et al. 2021

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

show abstract

“… Jia et al (2021) [ 280 ] Automatic COVID-19 diagnosis CXR and CT CNN CXR: 7592 CT: 104,009 CXR: 1770 CT: Not clear CXR: 99.6 CT: 99.3 The modified MobileNet and ResNet have been proposed. Chaudhary and Pachori (2021) [ 281 ] Automatic COVID-19 diagnosis CXR and CT CNN CXR: 1446 CT: 2481 CXR: 482 CT: 1252 CXR: 100 CT: 97.6 The combination of Fourier-Bessel series expansion-based image decomposition, different CNN architectures and various classifiers have been evaluated. Ibrahim et al (2021) [ 282 ] Automatic COVID-19 diagnosis CXR and CT CNN and GRU 33,676 4320 98.05 A multi-class classification method including VGG19 and some additional CNN layers shows the best performance.…”

Section: Automated Image Analysis Methods For Covid-19 Diagnosismentioning

confidence: 99%

Medical imaging and computational image analysis in COVID-19 diagnosis: A review

Nabavi

Ejmalian

Moghaddam

et al. 2021

Computers in Biology and Medicine

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Coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus. The disease presents with symptoms such as shortness of breath, fever, dry cough, and chronic fatigue, amongst others. The disease may be asymptomatic in some patients in the early stages, which can lead to increased transmission of the disease to others. This study attempts to review papers on the role of imaging and medical image computing in COVID-19 diagnosis. For this purpose, PubMed, Scopus and Google Scholar were searched to find related studies until the middle of 2021. The contribution of this study is four-fold: 1) to use as a tutorial of the field for both clinicians and technologists, 2) to comprehensively review the characteristics of COVID-19 as presented in medical images, 3) to examine automated artificial intelligence-based approaches for COVID-19 diagnosis, 4) to express the research limitations in this field and the methods used to overcome them. Using machine learning-based methods can diagnose the disease with high accuracy from medical images and reduce time, cost and error of diagnostic procedure. It is recommended to collect bulk imaging data from patients in the shortest possible time to improve the performance of COVID-19 automated diagnostic methods.

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“…Singh and Kolekar attempted to address the computational expensiveness of deep learning with a low-latency MobileNet model with an accuracy of 0.964 ( Singh & Kolekar, 2021 ). Chaudhary and Pachori introduced a Fourier-Bessel series decomposition method, which when combined when ResNet50 attained accuracies of 0.976 and sensitivity of 0.97 ( Chaudhary & Pachori, 2021 ). Table 1 summarizes the performance of recent works involving deep learning for COVID-19 classification.…”

Section: Related Workmentioning

confidence: 99%

Efficient and visualizable convolutional neural networks for COVID-19 classification using Chest CT

Garg

Salehi

Rocca

et al. 2022

Expert Systems with Applications

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With COVID-19 cases rising rapidly, deep learning has emerged as a promising diagnosis technique. However, identifying the most accurate models to characterize COVID-19 patients is challenging because comparing results obtained with different types of data and acquisition processes is non-trivial. In this paper we designed, evaluated, and compared the performance of 20 convolutional neutral networks in classifying patients as COVID-19 positive, healthy, or suffering from other pulmonary lung infections based on Chest CT scans, serving as the first to consider the EfficientNet family for COVID-19 diagnosis and employ intermediate activation maps for visualizing model performance. All models are trained and evaluated in Python using 4173 Chest CT images from the dataset entitled “A COVID multiclass dataset of CT scans,” with 2168, 758, and 1247 images of patients that are COVID-19 positive, healthy, or suffering from other pulmonary infections, respectively. EfficientNet-B5 was identified as the best model with an F1 score of 0.9769±0.0046, accuracy of 0.9759±0.0048, sensitivity of 0.9788±0.0055, specificity of 0.9730±0.0057, and precision of 0.9751± 0.0051. On an alternate 2-class dataset, EfficientNetB5 obtained an accuracy of 0.9845±0.0109, F1 score of 0.9599±0.0251, sensitivity of 0.9682±0.0099, specificity of 0.9883±0.0150, and precision of 0.9526 ± 0.0523. Intermediate activation maps and Gradient-weighted Class Activation Mappings offered human-interpretable evidence of the model’s perception of ground-class opacities and consolidations, hinting towards a promising use-case of artificial intelligence-assisted radiology tools. With a prediction speed of under 0.1 seconds on GPUs and 0.5 seconds on CPUs, our proposed model offers a rapid, scalable, and accurate diagnostic for COVID-19.

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FBSED based automatic diagnosis of COVID-19 using X-ray and CT images

Cited by 66 publications

References 32 publications

A transfer learning-based deep learning approach for automated COVID-19 diagnosis with audio data

A transfer learning-based deep learning approach for automated COVID-19 diagnosis with audio data

Medical imaging and computational image analysis in COVID-19 diagnosis: A review

Efficient and visualizable convolutional neural networks for COVID-19 classification using Chest CT

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