Deep Learning-based Classification of COVID-19 Lung Ultrasound for Tele-operative Robot-assisted diagnosis

Adedigba, Adeyinka P.; Adeshina, Steve A.

doi:10.1109/icmeas52683.2021.9692354

Cited by 7 publications

(19 citation statements)

References 21 publications

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“…The COVIDx-US dataset was used to implement various models, whose performance is illustrated by various evaluation metrics in Table 6. The models implemented by Adedigba and Adeshina (2021)…”

Section: Discussionmentioning

confidence: 99%

“…This process ensures a common intensity range across images and datasets. In most cases, all image data are converted to a common intensity range of [0, 1], or [0, 255] (Perera et al, 2021), followed by mean subtraction and division by standard deviation (Muhammad and Hossain, 2021;Adedigba and Adeshina, 2021;Quentin Muller et al, 2020;Roshankhah et al, 2021;Wang et al, 2021).…”

Section: Intensity Normalizationmentioning

confidence: 99%

“…We summarize the studies that used the COVIDx-US dataset to develop and evaluate AI methods in Table 6. Adedigba and Adeshina (2021)…”

Section: Studiesmentioning

confidence: 99%

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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

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Intensity Normalizationmentioning

confidence: 99%

See 1 more Smart Citation

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

“…The goal of these experiments was to diagnose COVID patients using different medical image modalities, as shown in Figure 1 . In Approach 1, we trained a ResNet model first to classify the images into their respective modality, and then the images were passed to our pre-trained models developed in [ 20 ] for CXR, [ 21 ] for CT scan, and [ 10 ] for LUS. We discovered that the overall performance of the model does not improve the results obtained in the previous works cited.…”

Section: Methodsmentioning

confidence: 99%

“…The summary and distribution of the datasets are presented in Table 1. The only data pre-processing carried out in this work is in line with [10], where the LUS videos were converted to images, and histogram equalization was used for intensity normalization and scaling.…”

Section: Datasetmentioning

confidence: 99%

Bag of Tricks for Improving Deep Learning Performance on Multimodal Image Classification

Adeshina

Adedigba

2022

Bioengineering

Self Cite

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A comprehensive medical image-based diagnosis is usually performed across various image modalities before passing a final decision; hence, designing a deep learning model that can use any medical image modality to diagnose a particular disease is of great interest. The available methods are multi-staged, with many computational bottlenecks in between. This paper presents an improved end-to-end method of multimodal image classification using deep learning models. We present top research methods developed over the years to improve models trained from scratch and transfer learning approaches. We show that when fully trained, a model can first implicitly discriminate the imaging modality and then diagnose the relevant disease. Our developed models were applied to COVID-19 classification from chest X-ray, CT scan, and lung ultrasound image modalities. The model that achieved the highest accuracy correctly maps all input images to their respective modality, then classifies the disease achieving overall 91.07% accuracy.

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Multi-modal lung ultrasound image classification by fusing image-based features and probe information

Okolo

Katsigiannis

Ramzan

2022

2022 IEEE 22nd International Conference on Bioinformatics and Bioengineering (BIBE)

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Lung ultrasound is a widely used portable, cheap, and non-invasive medical imaging technology that can be used to identify various lung pathologies. In this work, we propose a multi-modal approach for lung ultrasound image classification that combines image-based features with information about the type of ultrasound probe used to acquire the input image. Experiments on a large lung ultrasound image dataset that contains images acquired with a linear or a convex ultrasound probe demonstrated the superiority of the proposed approach for the task of classifying lung ultrasound images as "COVID-19", "Normal", "Pneumonia", or "Other", when compared to simply using image-based features. Classification accuracy reached 99.98% using the proposed combination of the Xception pretrained CNN model with the ultrasound probe information, as opposed to 96.81% when only the pre-trained EfficientNetB4 CNN model was used. Furthermore, the experimental results demonstrated a consistent improvement in classification performance when combining the examined base CNN models with probe information, indicating the efficiency of the proposed approach.

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Deep Learning-based Classification of COVID-19 Lung Ultrasound for Tele-operative Robot-assisted diagnosis

Cited by 7 publications

References 21 publications

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

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

Bag of Tricks for Improving Deep Learning Performance on Multimodal Image Classification

Multi-modal lung ultrasound image classification by fusing image-based features and probe information

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