Publication | Closed Access
Densely Connected Convolutional Networks (DenseNet) for Diagnosing Coronavirus Disease (COVID-19) from Chest X-ray Imaging
20
Citations
27
References
2021
Year
Unknown Venue
Convolutional Neural NetworkEngineeringMachine LearningChest X-ray ImagingDiagnostic ImagingImage AnalysisData SciencePattern RecognitionCoronavirus DiseaseConvolutional NetworksPromising Model AccuracyRadiologyHealth SciencesMedical ImagingFeature LearningMachine Learning ModelMedical Image ComputingDeep LearningDeep Learning MethodsEpidemiologyComputer VisionRadiomicsBiomedical ImagingComputer-aided DiagnosisTransfer LearningMedical Image Analysis
Since the beginning of the coronavirus disease (COVID-19) pandemic several machine learning and deep learning methods had been introduced to detect the infected patients using the X-Ray or CT scan images. Numerous sophisticated data-driven methods had been introduced to improve the performance and the accuracy of the diagnosis models. This paper proposes an improved densely connected convolutional networks (DenseNet) method based on transfer learning (TL) to enhance the model performance. The results show promising model accuracy.
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