Application of CycleGAN and transfer learning techniques for automated detection of COVID-19 using X-ray images

Bargshady, Ghazal; Zhou, Xujuan; Barua, Prabal Datta; Gururajan, Raj; Li, Yuefeng; Acharya, U. Rajendra

doi:10.1016/j.patrec.2021.11.020

Cited by 53 publications

(25 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Deep learning algorithms applied to health and medicine data have shown superior performance in diagnosing and detecting diseases such as skin cancer [ 37 ], chronic pain [ 38 ], diabetic retinopathy [ 39 ] and COVID-19 [ 40 ]. They have been applied for triaging referrals [ 4 ] as well.…”

Section: Discussionmentioning

confidence: 99%

Triaging Medical Referrals Based on Clinical Prioritisation Criteria Using Machine Learning Techniques

Wee

Zhou

Sun

et al. 2022

IJERPH

Self Cite

View full text Add to dashboard Cite

Triaging of medical referrals can be completed using various machine learning techniques, but trained models with historical datasets may not be relevant as the clinical criteria for triaging are regularly updated and changed. This paper proposes the use of machine learning techniques coupled with the clinical prioritisation criteria (CPC) of Queensland (QLD), Australia, to deliver better triaging for referrals in accordance with the CPC’s updates. The unique feature of the proposed model is its non-reliance on the past datasets for model training. Medical Natural Language Processing (NLP) was applied in the proposed approach to process the medical referrals, which are unstructured free text. The proposed multiclass classification approach achieved a Micro F1 score = 0.98. The proposed approach can help in the processing of two million referrals that the QLD health service receives annually; therefore, they can deliver better and more efficient health services.

show abstract

Section: Discussionmentioning

confidence: 99%

Triaging Medical Referrals Based on Clinical Prioritisation Criteria Using Machine Learning Techniques

Wee

Zhou

Sun

et al. 2022

IJERPH

Self Cite

View full text Add to dashboard Cite

show abstract

“…Then these features were concatenated to an autoencoder for reducing dimensionality. Bargshady et al [ 13 ] adopted CycleGAN for data augmentation and then used InceptionV3 to detect COVID-19. Irfan et al [ 32 ] proposed a hybrid deep neural network (HDNN) model, which is a mixture of two deep learning models (LSTM + CNN).…”

Section: Experiments and Discussionmentioning

confidence: 99%

“…Uçar and Korkmaz [ 12 ] introduced a model called COVIDiagnosis-Net, which fine-tuned SqueezeNet pretrained network with the Bayes optimization. Bargshady et al [ 13 ] adopted CycleGAN for data augmentation and then used InceptionV3 to detect COVID-19. Sahinbas and Catak [ 14 ] used X-ray images to detect COVID-19 with the well-known pretrained deep CNNs such as VGG-16, VGG-19, ResNet, DenseNet, and InceptionV3, and the experimental results have proven that the pretrained VGG-16 can detect COVID-19 with the highest classification performance.…”

Section: Related Workmentioning

confidence: 99%

A Novel Method for COVID-19 Detection Based on DCNNs and Hierarchical Structure

Zhang

Shi

et al. 2022

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

The worldwide outbreak of the new coronavirus disease (COVID-19) has been declared a pandemic by the World Health Organization (WHO). It has a devastating impact on daily life, public health, and global economy. Due to the highly infectiousness, it is urgent to early screening of suspected cases quickly and accurately. Chest X-ray medical image, as a diagnostic basis for COVID-19, arouses attention from medical engineering. However, due to small lesion difference and lack of training data, the accuracy of detection model is insufficient. In this work, a transfer learning strategy is introduced to hierarchical structure to enhance high-level features of deep convolutional neural networks. The proposed framework consisting of asymmetric pretrained DCNNs with attention networks integrates various information into a wider architecture to learn more discriminative and complementary features. Furthermore, a novel cross-entropy loss function with a penalty term weakens misclassification. Extensive experiments are implemented on the COVID-19 dataset. Compared with the state-of-the-arts, the effectiveness and high performance of the proposed method are demonstrated.

show abstract

“…Bargshady et al [19] proposed a CycleGAN-Inception model which they integrate GAN and pretrained inception CNN to diagnose COVID-19. In their approach, GAN is utilized to compensate less training examples by augmenting it during training.…”

Section: A X-ray Based Diagnosis Of Covid-19mentioning

confidence: 99%

A Review on Deep Learning based diagnosis of COVID-19 from X-ray and CT Images

Kumar

2022

2022 International Mobile and Embedded Technology Conference (MECON)

View full text Add to dashboard Cite

More than 400 million cases of the new coronavirus (COVID-19) have been confirmed since December 2019 in more than 200 countries. Since the spread of original COVID-19 virus SARS-CoV-2, thousands of mutations have been discovered. The most dominant ones are Alpha, Beta, Gama, Delta and Omicron variants, with the Omicron variant rapidly spreading and dominating the current phase of the COVID wave across the globe. It needs early detection and self-isolation to contain the virus. Molecular tests like rRT-PCR are common for its detection. However, with the current spreading rate and lack of availability of large-scale testing laboratories, rapid diagnosis has become difficult. COVID-19 diagnosis from CT and X-ray images using deep learning techniques has been the subject of a lot of research in the last two years. This work presents a review of these studies sourced from top databases such as Web of Science and highlights challenges and research gaps with future research directions.

show abstract

Application of CycleGAN and transfer learning techniques for automated detection of COVID-19 using X-ray images

Cited by 53 publications

References 16 publications

Triaging Medical Referrals Based on Clinical Prioritisation Criteria Using Machine Learning Techniques

Triaging Medical Referrals Based on Clinical Prioritisation Criteria Using Machine Learning Techniques

A Novel Method for COVID-19 Detection Based on DCNNs and Hierarchical Structure

A Review on Deep Learning based diagnosis of COVID-19 from X-ray and CT Images

Contact Info

Product

Resources

About