Covid-19 Ultrasound image classification using SVM based on kernels deduced from Convolutional neural network

Al-jumaili, Saif; Duru, Adil Deniz; Uçan, Osman N.

doi:10.1109/ismsit52890.2021.9604551

Cited by 13 publications

(15 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Studies using POCUS dataset reported impressive results across various metrics and methodologies. For instance, Al-Jumaili et al [68] achieved accuracy, precision, and F1-score of above 99%. Awasthi et al [72] developed a power and memory-efficient network that attained an impressive highest accuracy of 83.2%.…”

Section: Discussionmentioning

confidence: 99%

“…To overcome this limitation, deep learning on medical imaging often leverages the transfer learning strategy, where the deep model is pre-trained on a much larger natural image dataset and then finetuned on the smaller medical data. This transfer learning strategy is also used in many articles (for example, Nabalamba [49], Rojas-Azabache et al [50], Diaz-Escobar et al [67], Al-Jumaili et al [68], Barros et al [69]) we reviewed in this study. In addition, many studies in this review (for example, Born et al [12], Diaz-Escobar et al [67]) used cross-validation techniques to avoid overfitting.…”

Section: Ai In Ultrasound Covid-2019 Studiesmentioning

confidence: 99%

“…Fully connected layers are responsible for the final classification or regression task, combining the extracted features to make predictions. Most of the articles we reviewed in this study used different types of CNNs, for example, SqueezeNet [59], MobileNetV2 [59], VGG-16 [12,49,74], VGG-19 [49,66], ResNet-18 [37,42,68], ResNet-50 [42,68], ResNet-152V2 [66], DenseNet-201 [66], InceptionV3 [73], GoogleNet [68], and NasNetMobile [66,68].…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

“…The SVM regression algorithm aims to find a balance between fitting the data closely and controlling the complexity of the model to avoid overfitting. Several articles we reviewed in this study used SVM [16,40,43,68].…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

See 3 more Smart Citations

Ultrasound-Based AI for COVID-19 Detection: A Comprehensive Review of Public and Private Lung Ultrasound Datasets and Studies

Morshed¹,

Shihab²,

Jahin³

et al. 2023

Preprint

View full text Add to dashboard Cite

The COVID-19 pandemic has affected millions of people globally, with respiratory organs being strongly affected in individuals with comorbidities. Medical imaging-based diagnosis and prognosis have become increasingly popular in clinical settings to detect COVID-19 lung infections. Among various medical imaging modalities, ultrasound stands out as low-cost, mobile, and radiation-safe imaging technology. In this comprehensive review, we focus on ultrasound-based AI studies for COVID-19 detection that use public or private lung ultrasound datasets. We surveyed articles that used publicly available lung ultrasound datasets for COVID-19 and reviewed publicly available datasets and organize ultrasound-based AI studies per dataset. We analyzed and tabulated studies in several dimensions, such as data preprocessing, AI models, cross-validation, and evaluation criteria. In total, we reviewed 42 articles, where 28 articles used public datasets, and the rest used private data. Our findings suggest that ultrasound-based AI studies for the detection of COVID-19 have great potential for clinical use, especially for children and pregnant women. Our review also provides a useful summary for future researchers and clinicians who may be interested in the field.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Ai In Ultrasound Covid-2019 Studiesmentioning

confidence: 99%

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

See 2 more Smart Citations

Ultrasound-Based AI for COVID-19 Detection: A Comprehensive Review of Public and Private Lung Ultrasound Datasets and Studies

Morshed¹,

Shihab²,

Jahin³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…An SVM classifier yielded a high area under the curve (AUC) of 0.96, with a sensitivity of 0.93 and specificity of 1 [ 69 ]. One could also separate the evaluation into two steps of feature extraction and image classification [ 70 ]. Four CNN models (Resnet18, Resnet50, GoogleNet, and NASNet-Mobile) were tried to extract features from LUS images and then fed the features to the SVM classifier.…”

Section: Machine Learning In Covid-19 Lusmentioning

confidence: 99%

Review of Machine Learning in Lung Ultrasound in COVID-19 Pandemic

et al. 2022

View full text Add to dashboard Cite

Ultrasound imaging of the lung has played an important role in managing patients with COVID-19–associated pneumonia and acute respiratory distress syndrome (ARDS). During the COVID-19 pandemic, lung ultrasound (LUS) or point-of-care ultrasound (POCUS) has been a popular diagnostic tool due to its unique imaging capability and logistical advantages over chest X-ray and CT. Pneumonia/ARDS is associated with the sonographic appearances of pleural line irregularities and B-line artefacts, which are caused by interstitial thickening and inflammation, and increase in number with severity. Artificial intelligence (AI), particularly machine learning, is increasingly used as a critical tool that assists clinicians in LUS image reading and COVID-19 decision making. We conducted a systematic review from academic databases (PubMed and Google Scholar) and preprints on arXiv or TechRxiv of the state-of-the-art machine learning technologies for LUS images in COVID-19 diagnosis. Openly accessible LUS datasets are listed. Various machine learning architectures have been employed to evaluate LUS and showed high performance. This paper will summarize the current development of AI for COVID-19 management and the outlook for emerging trends of combining AI-based LUS with robotics, telehealth, and other techniques.

show abstract