Adversarial Neural Network Classifiers for COVID-19 Diagnosis in Ultrasound Images

Karar, Mohamed Esmail; Shouman, Marwa A.; Chalopin, Claire

doi:10.32604/cmc.2022.018564

Cited by 8 publications

(12 citation statements)

References 35 publications

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“…However, other image dimensions are also found for ultrasound COVID-19 studies. For example, Karar et al (2021b) resized all ultrasound images to 28×28 pixels to avoid a higher computational overhead. Furthermore, Mateu et al ( 2022), Durrani et al (2022), Muhammad and Hossain (2021), and Gare et al ( 2021) resized their ultrasound images to 254×254, 806×550, 512×512, and 624×464 pixels, respectively.…”

Section: Image Resizingmentioning

confidence: 99%

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: Image Resizingmentioning

confidence: 99%

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 augmented data were then used to improve the training of different CNNs to diagnose COVID-19. In addition, 3 (5%) studies used GANs for segmentation of the lung region within the chest radiology images [37,51,57], 3 (5%) studies used GANs for superresolution to improve the quality of the images before using them for diagnosis purposes [30,44,68], 5 (9%) studies used GANs for the diagnosis of COVID-19 [20,58,69,70,72], 2 (4%) studies used GANs for feature extraction from images [19,47], and 1 (2%) study used a GAN-based method for prognosis of COVID-19 [22]. The prevalent mode of imaging is the use of 2D imaging data, and 1 (2%) study reported a GAN-based method for synthesizing 3D data [49].…”

Section: Application Of the Studiesmentioning

confidence: 99%

“…The cycleGAN is an image translation GAN that does not require paired data to transform images from one domain to another. Other popular types of GANs were conditional GAN used by 9 (16%) studies [18,22,24,25,33,37,41,57,60], deep convolutional GAN used by 4 (7%) studies [21,38,43,67], and auxiliary classifier GAN used by 4 (7%) studies [32,40,55,69]. The superresolution GAN was used by 2 (4%) studies [44,68], and 1 (2%) study reported the use of multiple GANs, namely Wassertein GAN, auxiliary classifier GAN, and deep convolutional GAN, and compared their performances for improving the quality of images [31].…”

Section: Application Of the Studiesmentioning

confidence: 99%

“…Of the 57 studies, only 10 (18%) [18,19,26,27,30,34,43,[61][62][63][64][65][66][67][68][69][70][71][72][73] reported changes to the architecture of the GAN they were using. In the rest of the studies, no major changes were reported to the architecture of the GAN.…”

Section: Application Of the Studiesmentioning

confidence: 99%

“…In total, 29 (51%) studies used X-ray images of lungs [20,21, 25, 27-29, 31, 32, 35, 37, 40-43, 45, 50, 52, 54, 56, 57, 59, 60, 62, 64, 65, 67, 70, 73, 74], while 21 (37%) studies used CT images [18,19,[22][23][24]26,30,33,34,36,38,48,49,51,53,55,58,61,63,66,71], and 6 (11%) studies reported the use of both X-ray and CT images [39,44,46,47,68,72]. Only 1 (2%) study used ultrasound images for COVID-19 diagnosis [69], which shows that ultrasound is not a popular imaging modality for training GANs and other deep learning models for COVID-19 detection (also see Figure 4). Of the 57 studies, most (n=47, 82%) used image data sets that are publicly available.…”

Section: Characteristics Of the Data Setsmentioning

confidence: 99%

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Combating COVID-19 Using Generative Adversarial Networks and Artificial Intelligence for Medical Images: Scoping Review (Preprint)

Ali¹,

Shah²

2022

Preprint

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BACKGROUND Research on the diagnosis of COVID-19 using lung images is limited by the scarcity of imaging data. Generative adversarial networks (GANs) are popular for synthesis and data augmentation. GANs have been explored for data augmentation to enhance the performance of artificial intelligence (AI) methods for the diagnosis of COVID-19 within lung computed tomography (CT) and X-ray images. However, the role of GANs in overcoming data scarcity for COVID-19 is not well understood. OBJECTIVE This review presents a comprehensive study on the role of GANs in addressing the challenges related to COVID-19 data scarcity and diagnosis. It is the first review that summarizes different GAN methods and lung imaging data sets for COVID-19. It attempts to answer the questions related to applications of GANs, popular GAN architectures, frequently used image modalities, and the availability of source code. METHODS A search was conducted on 5 databases, namely PubMed, IEEEXplore, Association for Computing Machinery (ACM) Digital Library, Scopus, and Google Scholar. The search was conducted from October 11-13, 2021. The search was conducted using intervention keywords, such as “generative adversarial networks” and “GANs,” and application keywords, such as “COVID-19” and “coronavirus.” The review was performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines for systematic and scoping reviews. Only those studies were included that reported GAN-based methods for analyzing chest X-ray images, chest CT images, and chest ultrasound images. Any studies that used deep learning methods but did not use GANs were excluded. No restrictions were imposed on the country of publication, study design, or outcomes. Only those studies that were in English and were published from 2020 to 2022 were included. No studies before 2020 were included. RESULTS This review included 57 full-text studies that reported the use of GANs for different applications in COVID-19 lung imaging data. Most of the studies (n=42, 74%) used GANs for data augmentation to enhance the performance of AI techniques for COVID-19 diagnosis. Other popular applications of GANs were segmentation of lungs and superresolution of lung images. The cycleGAN and the conditional GAN were the most commonly used architectures, used in 9 studies each. In addition, 29 (51%) studies used chest X-ray images, while 21 (37%) studies used CT images for the training of GANs. For the majority of the studies (n=47, 82%), the experiments were conducted and results were reported using publicly available data. A secondary evaluation of the results by radiologists/clinicians was reported by only 2 (4%) studies. CONCLUSIONS Studies have shown that GANs have great potential to address the data scarcity challenge for lung images in COVID-19. Data synthesized with GANs have been helpful to improve the training of the convolutional neural network (CNN) models trained for the diagnosis of COVID-19. In addition, GANs have also contributed to enhancing the CNNs’ performance through the superresolution of the images and segmentation. This review also identified key limitations of the potential transformation of GAN-based methods in clinical applications.

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Automated classification of urine biomarkers to diagnose pancreatic cancer using 1-D convolutional neural networks

2023

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Background Early diagnosis of Pancreatic Ductal Adenocarcinoma (PDAC) is the main key to surviving cancer patients. Urine proteomic biomarkers which are creatinine, LYVE1, REG1B, and TFF1 present a promising non-invasive and inexpensive diagnostic method of the PDAC. Recent utilization of both microfluidics technology and artificial intelligence techniques enables accurate detection and analysis of these biomarkers. This paper proposes a new deep-learning model to identify urine biomarkers for the automated diagnosis of pancreatic cancers. The proposed model is composed of one-dimensional convolutional neural networks (1D-CNNs) and long short-term memory (LSTM). It can categorize patients into healthy pancreas, benign hepatobiliary disease, and PDAC cases automatically. Results Experiments and evaluations have been successfully done on a public dataset of 590 urine samples of three classes, which are 183 healthy pancreas samples, 208 benign hepatobiliary disease samples, and 199 PDAC samples. The results demonstrated that our proposed 1-D CNN + LSTM model achieved the best accuracy score of 97% and the area under curve (AUC) of 98% versus the state-of-the-art models to diagnose pancreatic cancers using urine biomarkers. Conclusion A new efficient 1D CNN-LSTM model has been successfully developed for early PDAC diagnosis using four proteomic urine biomarkers of creatinine, LYVE1, REG1B, and TFF1. This developed model showed superior performance on other machine learning classifiers in previous studies. The main prospect of this study is the laboratory realization of our proposed deep classifier on urinary biomarker panels for assisting diagnostic procedures of pancreatic cancer patients.

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Adversarial Neural Network Classifiers for COVID-19 Diagnosis in Ultrasound Images

Cited by 8 publications

References 35 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

Combating COVID-19 Using Generative Adversarial Networks and Artificial Intelligence for Medical Images: Scoping Review (Preprint)

Automated classification of urine biomarkers to diagnose pancreatic cancer using 1-D convolutional neural networks

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