Detection of COVID‐19 from CT Lung Scans Using Transfer Learning

Lawton, Sahil; Viriri, Serestina

doi:10.1155/2021/5527923

Cited by 34 publications

(25 citation statements)

References 21 publications

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“…The DenseNet201 model and KNN algorithm had the best result when combining pre-trained models with ML algorithms and were able to achieve very good accuracy. An integrated approach of five different transfer learning architectures such as Densenet 201, VGG19, Efficient Net, Mobile net, and ResNet to detect Covid-19 images [8] . The findings of this research reveal that transfer learning-based frameworks could be a viable alternative to the existing methods for detecting the occurrence of the infection in victims.…”

Section: Related Workmentioning

confidence: 99%

Effect of image transformation on EfficientNet model for COVID-19 CT image classification

Ebenezer

Kanmani²,

Sivakumar

et al. 2022

Materials Today: Proceedings

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The Novel Corona Virus 2019 has drastically affected millions of people all around the world and was a huge threat to the human race since its evolution in 2019. Chest CT images are considered to be one of the indicative sources for diagnosis of COVID-19 by most of the researchers in the research community. Several researchers have proposed various models for the prediction of Covid- 19 using CT images using Artificial Intelligence based algorithms [19] , [20] , [21] . EfficientNet is one of the powerful Convolutional Neural Network models proposed by Mingxing Tan and Quoc [18] . The objective of this study is to explore the effect of image enhancement algorithms such as Laplace transform, Wavelet transforms, Adaptive gamma correction and Contrast limited adaptive histogram equalization (CLAHE) on Chest CT images for the classification of Covid-19 using the EfficientNet algorithm. SARS- COV-2 [14] dataset is used in this study. The images were preprocessed and brightness augmented. The EfficientNet algorithm is implemented and the performance is evaluated by adding the four image enhancement algorithms. The CLAHE based EfficientNet model yielded an accuracy of 94.56%, precision of 95%, recall of 91%, and F1 of 93%. This study shows that adding a CLAHE image enhancement to the EfficientNet model improves the performance of the powerful Convolutional Neural Network model in classifying the CT images for Covid-19.

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Section: Related Workmentioning

confidence: 99%

Effect of image transformation on EfficientNet model for COVID-19 CT image classification

Ebenezer

Kanmani²,

Sivakumar

et al. 2022

Materials Today: Proceedings

View full text Add to dashboard Cite

show abstract

“…The reasons for this would require further study but it may be conceptually important that the performance of the model depends on factors other than image resolution [15] or set size alone, with the network architecture possibly also an important factor contributing to model performance. The E cientNet [6] family of models has shown among other Convolutional Neural Networks e cacy in terms of performance and speed using commercially available GPU processing capabilities in the classi cation of skin lesions [16], CT lung scans [17] and diabetic retinopathy [18] but this is the probably one of the rst papers employing this model in paediatric elbow radiographs. In this study, a lower powered B1 version of the model was employed as compared to higher (i.e.…”

Section: Discussionmentioning

confidence: 99%

Developing Deep Learning Models for the Classification of Pediatric Elbow Radiographic Abnormalities of Comparable Performance to Physicians: Strategies for Model Optimization With Small Sized Development Sets

Tan

Chua

Fan

et al. 2022

Preprint

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BackgroundTo compare the performance of an AI model based on strategies designed to overcome small sized development sets to pediatric ER physicians at a classification triage task of pediatric elbow radiographs. Methods1,314 pediatric elbow lateral radiographs (mean age: 8.2 years) were retrospectively retrieved, binomially classified based on their annotation as normal or abnormal (with pathology), and randomly partitioned into a development set (993 images), tuning set (109 images), second tuning set (100 images) and test set (112 images). The AI model was trained on the development set and utilized the EfficientNet B1 compound scaling network architecture and online augmentations. Its performance on the test set was compared to a group of five physicians (inter-rater agreement: fair). Statistical analysis: AUC of AI model - DeLong method. Performance of AI model and physician groups - McNemar test. ResultsAccuracy of the model on the test set - 0.804 (95% CI, 0.718 - 0.873), AUROC - 0.872 (95% CI, 0.831 - 0.947). AI model performance compared to the physician group on the test set - sensitivity 0.790 (95% CI 0.684 to 0.895) vs 0.649 (95% CI 0.525 to 0.773), p value 0.088; specificity 0.818 (95% CI 0.716 to 0.920) vs 0.873 (95% CI 0.785 to 0.961), p value 0.439.ConclusionsThe AI model for elbow radiograph triage designed with strategies to optimize performance for a small sized development set showed comparable performance to physicians.

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“…Thus, DL techniques are regularly utilized to automatically extract features to classify cases infected with COVID-19. Components of these systems are built using a pre-trained model that incorporates transfer learning [26][27][28], and a few are introduced through personalized networks [29][30][31].…”

Section: Basic and Backgroundmentioning

confidence: 99%

Study of Different Deep Learning Methods for Coronavirus (COVID-19) Pandemic: Taxonomy, Survey and Insights

Awassa

Jdey

Dhahri

et al. 2022

Sensors

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COVID-19 has evolved into one of the most severe and acute illnesses. The number of deaths continues to climb despite the development of vaccines and new strains of the virus have appeared. The early and precise recognition of COVID-19 are key in viably treating patients and containing the pandemic on the whole. Deep learning technology has been shown to be a significant tool in diagnosing COVID-19 and in assisting radiologists to detect anomalies and numerous diseases during this epidemic. This research seeks to provide an overview of novel deep learning-based applications for medical imaging modalities, computer tomography (CT) and chest X-rays (CXR), for the detection and classification COVID-19. First, we give an overview of the taxonomy of medical imaging and present a summary of types of deep learning (DL) methods. Then, utilizing deep learning techniques, we present an overview of systems created for COVID-19 detection and classification. We also give a rundown of the most well-known databases used to train these networks. Finally, we explore the challenges of using deep learning algorithms to detect COVID-19, as well as future research prospects in this field.

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Detection of COVID‐19 from CT Lung Scans Using Transfer Learning

Cited by 34 publications

References 21 publications

Effect of image transformation on EfficientNet model for COVID-19 CT image classification

Effect of image transformation on EfficientNet model for COVID-19 CT image classification

Developing Deep Learning Models for the Classification of Pediatric Elbow Radiographic Abnormalities of Comparable Performance to Physicians: Strategies for Model Optimization With Small Sized Development Sets

Study of Different Deep Learning Methods for Coronavirus (COVID-19) Pandemic: Taxonomy, Survey and Insights

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