COVID-19 Detection Using Deep Learning Algorithm on Chest X-ray Images

Akter, Shamima; Shamrat, F. M. Javed Mehedi; Chakraborty, Sovon; Karim, Asif; Azam, Sami

doi:10.3390/biology10111174

Cited by 97 publications

(55 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Deep learning models trained on the limited datasets are not generalized; thus, such models are not reliable. It has been found through the literature, that data augmentation techniques can be used to resolve small dataset issues [ 34 ]. Furthermore, the already available research is more focused on the binary classification of COVID-19 [ 18 , 19 , 20 , 21 , 22 ] and limited research has been conducted for multiclass classification of COVID-19 [ 28 , 29 , 30 , 31 , 32 , 33 ].…”

Section: Related Workmentioning

confidence: 99%

Chest X-ray Classification for the Detection of COVID-19 Using Deep Learning Techniques

Khan

Rehman

Ahmed

et al. 2022

Sensors

View full text Add to dashboard Cite

Recent technological developments pave the path for deep learning-based techniques to be used in almost every domain of life. The precision of deep learning techniques make it possible for these to be used in the medical field for the classification and detection of various diseases. Recently, the coronavirus (COVID-19) pandemic has put a lot of pressure on the health system all around the world. The diagnosis of COVID-19 is possible by PCR testing and medical imagining. Since COVID-19 is highly contagious, diagnosis using chest X-ray is considered safe in various situations. In this study, a deep learning-based technique is proposed to classify COVID-19 infection from other non-COVID-19 infections. To classify COVID-19, three different pre-trained models named EfficientNetB1, NasNetMobile and MobileNetV2 are used. The augmented dataset is used for training deep learning models while two different training strategies have been used for classification. In this study, not only are the deep learning model fine-tuned but also the hyperparameters are fine-tuned, which significantly improves the performance of the fine-tuned deep learning models. Moreover, the classification head is regularized to improve the performance. For the evaluation of the proposed techniques, several performance parameters are used to gauge the performance. EfficientNetB1 with regularized classification head outperforms the other models. The proposed technique successfully classifies four classes that include COVID-19, viral pneumonia, lung opacity, and normal, with an accuracy of 96.13%. The proposed technique shows superiority in terms of accuracy when compared with recent techniques present in the literature.

show abstract

Section: Related Workmentioning

confidence: 99%

Chest X-ray Classification for the Detection of COVID-19 Using Deep Learning Techniques

Khan

Rehman

Ahmed

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…DL architectures have shown excellent performance in the medical and commercial fields [ 21 , 22 , 23 , 24 , 25 ]. Therefore, DL is primarily employed in the detection of COVID-19 infection and drug repurposing in diverse ways [ 26 , 27 , 28 , 29 ]. Several researchers have employed CNN to speed up the analysis of COVID-19 infected images [ 30 , 31 , 32 , 33 , 34 ].…”

Section: Introductionmentioning

confidence: 99%

COVID-19 Detection in Chest X-ray Images Using a New Channel Boosted CNN

Khan¹,

Sohail²,

Khan

2022

Diagnostics

View full text Add to dashboard Cite

COVID-19 is a respiratory illness that has affected a large population worldwide and continues to have devastating consequences. It is imperative to detect COVID-19 at the earliest opportunity to limit the span of infection. In this work, we developed a new CNN architecture STM-RENet to interpret the radiographic patterns from X-ray images. The proposed STM-RENet is a block-based CNN that employs the idea of split–transform–merge in a new way. In this regard, we have proposed a new convolutional block STM that implements the region and edge-based operations separately, as well as jointly. The systematic use of region and edge implementations in combination with convolutional operations helps in exploring region homogeneity, intensity inhomogeneity, and boundary-defining features. The learning capacity of STM-RENet is further enhanced by developing a new CB-STM-RENet that exploits channel boosting and learns textural variations to effectively screen the X-ray images of COVID-19 infection. The idea of channel boosting is exploited by generating auxiliary channels from the two additional CNNs using Transfer Learning, which are then concatenated to the original channels of the proposed STM-RENet. A significant performance improvement is shown by the proposed CB-STM-RENet in comparison to the standard CNNs on three datasets, especially on the stringent CoV-NonCoV-15k dataset. The good detection rate (97%), accuracy (96.53%), and reasonable F-score (95%) of the proposed technique suggest that it can be adapted to detect COVID-19 infected patients.

show abstract

“…In severe circumstances, Computed Tomography (CT) may provide a succession of body scans that are then combined to create a three-dimensional X-ray picture analyzed by a computer. However, a normal X-ray is quicker, simpler, less expensive, and less invasive than a CT scan [ 7 ]. In the majority of instances, X-rays are insufficient to provide a diagnosis; CT scans are often required to confirm the diagnosis [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

“…CNNs have been important in the extraction of features and in the learning of patterns that allowed prediction [ 12 ]. AI has matured to the point that it can be incorporated with cutting-edge machine learning and deep learning algorithms in a variety of disciplines, including health [ 7 ], biometrics [ 13 ], agriculture [ 14 ], cloud computing [ 15 ], and renewable energy [ 16 ]. Numerous advantages are associated with AI technology in medicine.…”

Section: Introductionmentioning

confidence: 99%

LungNet22: A Fine-Tuned Model for Multiclass Classification and Prediction of Lung Disease Using X-ray Images

Shamrat

Azam

Karim

et al. 2022

JPM

Self Cite

View full text Add to dashboard Cite

In recent years, lung disease has increased manyfold, causing millions of casualties annually. To combat the crisis, an efficient, reliable, and affordable lung disease diagnosis technique has become indispensable. In this study, a multiclass classification of lung disease from frontal chest X-ray imaging using a fine-tuned CNN model is proposed. The classification is conducted on 10 disease classes of the lungs, namely COVID-19, Effusion, Tuberculosis, Pneumonia, Lung Opacity, Mass, Nodule, Pneumothorax, and Pulmonary Fibrosis, along with the Normal class. The dataset is a collective dataset gathered from multiple sources. After pre-processing and balancing the dataset with eight augmentation techniques, a total of 80,000 X-ray images were fed to the model for classification purposes. Initially, eight pre-trained CNN models, AlexNet, GoogLeNet, InceptionV3, MobileNetV2, VGG16, ResNet 50, DenseNet121, and EfficientNetB7, were employed on the dataset. Among these, the VGG16 achieved the highest accuracy at 92.95%. To further improve the classification accuracy, LungNet22 was constructed upon the primary structure of the VGG16 model. An ablation study was used in the work to determine the different hyper-parameters. Using the Adam Optimizer, the proposed model achieved a commendable accuracy of 98.89%. To verify the performance of the model, several performance matrices, including the ROC curve and the AUC values, were computed as well.

show abstract

COVID-19 Detection Using Deep Learning Algorithm on Chest X-ray Images

Cited by 97 publications

References 55 publications

Chest X-ray Classification for the Detection of COVID-19 Using Deep Learning Techniques

Chest X-ray Classification for the Detection of COVID-19 Using Deep Learning Techniques

COVID-19 Detection in Chest X-ray Images Using a New Channel Boosted CNN

LungNet22: A Fine-Tuned Model for Multiclass Classification and Prediction of Lung Disease Using X-ray Images

Contact Info

Product

Resources

About