FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4383603003> ?p ?o ?g. }

• W4383603003 endingPage "50" @default.
• W4383603003 startingPage "27" @default.
• W4383603003 abstract "The COVID-19 pandemic caused by the SARS-CoV-2 virus, which arose in Wuhan, Hubei province, China, on 31 December 2019 and rapidly spread to six continents and hundreds of countries, will go down in history as the first pandemic produced by coronaviruses. The World Health Organization (WHO) classified COVID-19 as a “pandemic” on March 11, 2020. To avoid the future spread of this pandemic and to promptly treat infected patients, it is crucial to find positive cases as early as feasible. To prevent or slow the spread of COVID-19 infection, an accurate early diagnosis is crucial. Medical imaging techniques, such as X-rays and computed tomography (CT), play a crucial role in the global fight against COVID-19. In this paper, preprocessing techniques have been applied to X-RAY images to improve classification performance. Gradient operator used as Sobel operator to highlight dot regions in X-RAY images and reduce the number of house gray tones The application of artificial intelligence and machine learning techniques to radiological images helps to detect this disease accurately and quickly. In this study, 13 different deep learning techniques were studied using Chi-square, NCA, mRMR and ReliefF feature selection algorithms. A detailed experimental study was carried out including AlexNet, EfficientNet B0, GoogleNet, Inception ResNet-v2, Inception-v3, DenseNet201, MobilevNet2, Nasnet-Large, ResNet18, ResNet50, ResNet101, VGG16 and VGG19 deep learning models and artificial neural networks, nearest neighbor algorithm and traditional machines such as support vector machine, decision tree and neural network including learning methods. Classifiers built using pre-trained models were trained and the accuracy rates of the resulting models for the test data was calculated as 96.42%, 96.61%, 96.51%, 96.06%, 95.00%, 96.14%, 96.14%, 96.32%, 96.04%, 97.08%, 96.04%, 96.42%, 95.66% and 98.21% for models based on AlexNet, DenseNet EfficientNet B0, GoogleNet, Inception ResNet-v2, Inception-v3, MobilevNet2, Nasnet-Large, ResNet18, ResNet50, ResNet101, VGG16, VGG19, and ResNet50 + AlexNet Cubic SVM with mRMR feature selections. In experimental studies, the highest accuracy value was obtained in Cubic SVM as 98.21% by applying mRMR feature selections to the features obtained from ResNet50 and AlexNet models. These results show that classifiers created using pre-trained models can assist physicians in the diagnosis of the COVID-19 pandemic." @default.
• W4383603003 created "2023-07-08" @default.
• W4383603003 creator A5088995599 @default.
• W4383603003 date "2023-01-01" @default.
• W4383603003 modified "2023-10-18" @default.
• W4383603003 title "Deep Learning-Based Approaches Using Feature Selection Methods for Automatic Diagnosis of COVID-19 Disease from X-Ray Images" @default.
• W4383603003 cites W1500895378 @default.
• W4383603003 cites W1550861865 @default.
• W4383603003 cites W1990729297 @default.
• W4383603003 cites W2097117768 @default.
• W4383603003 cites W2110840436 @default.
• W4383603003 cites W2127731579 @default.
• W4383603003 cites W2131987814 @default.
• W4383603003 cites W2149471024 @default.
• W4383603003 cites W2151570219 @default.
• W4383603003 cites W2154053567 @default.
• W4383603003 cites W2194775991 @default.
• W4383603003 cites W2618530766 @default.
• W4383603003 cites W2952416588 @default.
• W4383603003 cites W2963446712 @default.
• W4383603003 cites W2964350391 @default.
• W4383603003 cites W2964477674 @default.
• W4383603003 cites W3004227146 @default.
• W4383603003 cites W3017243633 @default.
• W4383603003 cites W3023594394 @default.
• W4383603003 cites W3034393064 @default.
• W4383603003 cites W3043129391 @default.
• W4383603003 cites W3083753334 @default.
• W4383603003 cites W3093415991 @default.
• W4383603003 cites W3125811498 @default.
• W4383603003 cites W3129576291 @default.
• W4383603003 cites W3131964007 @default.
• W4383603003 cites W3133191822 @default.
• W4383603003 cites W3139487216 @default.
• W4383603003 cites W3162351260 @default.
• W4383603003 cites W3179841826 @default.
• W4383603003 cites W3187766292 @default.
• W4383603003 cites W3196669330 @default.
• W4383603003 cites W3212912762 @default.
• W4383603003 cites W4212883601 @default.
• W4383603003 cites W4220685350 @default.
• W4383603003 cites W4226315139 @default.
• W4383603003 cites W4230674625 @default.
• W4383603003 doi "https://doi.org/10.1007/978-981-99-3784-4_2" @default.
• W4383603003 hasPublicationYear "2023" @default.
• W4383603003 type Work @default.
• W4383603003 citedByCount "0" @default.
• W4383603003 crossrefType "book-chapter" @default.
• W4383603003 hasAuthorship W4383603003A5088995599 @default.
• W4383603003 hasConcept C108583219 @default.
• W4383603003 hasConcept C113238511 @default.
• W4383603003 hasConcept C115961682 @default.
• W4383603003 hasConcept C119857082 @default.
• W4383603003 hasConcept C12267149 @default.
• W4383603003 hasConcept C138885662 @default.
• W4383603003 hasConcept C142724271 @default.
• W4383603003 hasConcept C148483581 @default.
• W4383603003 hasConcept C153180895 @default.
• W4383603003 hasConcept C154945302 @default.
• W4383603003 hasConcept C193536780 @default.
• W4383603003 hasConcept C2776401178 @default.
• W4383603003 hasConcept C2779134260 @default.
• W4383603003 hasConcept C3008058167 @default.
• W4383603003 hasConcept C30703548 @default.
• W4383603003 hasConcept C34736171 @default.
• W4383603003 hasConcept C41008148 @default.
• W4383603003 hasConcept C41895202 @default.
• W4383603003 hasConcept C50644808 @default.
• W4383603003 hasConcept C524204448 @default.
• W4383603003 hasConcept C71924100 @default.
• W4383603003 hasConcept C84525736 @default.
• W4383603003 hasConcept C89623803 @default.
• W4383603003 hasConcept C9417928 @default.
• W4383603003 hasConceptScore W4383603003C108583219 @default.
• W4383603003 hasConceptScore W4383603003C113238511 @default.
• W4383603003 hasConceptScore W4383603003C115961682 @default.
• W4383603003 hasConceptScore W4383603003C119857082 @default.
• W4383603003 hasConceptScore W4383603003C12267149 @default.
• W4383603003 hasConceptScore W4383603003C138885662 @default.
• W4383603003 hasConceptScore W4383603003C142724271 @default.
• W4383603003 hasConceptScore W4383603003C148483581 @default.
• W4383603003 hasConceptScore W4383603003C153180895 @default.
• W4383603003 hasConceptScore W4383603003C154945302 @default.
• W4383603003 hasConceptScore W4383603003C193536780 @default.
• W4383603003 hasConceptScore W4383603003C2776401178 @default.
• W4383603003 hasConceptScore W4383603003C2779134260 @default.
• W4383603003 hasConceptScore W4383603003C3008058167 @default.
• W4383603003 hasConceptScore W4383603003C30703548 @default.
• W4383603003 hasConceptScore W4383603003C34736171 @default.
• W4383603003 hasConceptScore W4383603003C41008148 @default.
• W4383603003 hasConceptScore W4383603003C41895202 @default.
• W4383603003 hasConceptScore W4383603003C50644808 @default.
• W4383603003 hasConceptScore W4383603003C524204448 @default.
• W4383603003 hasConceptScore W4383603003C71924100 @default.
• W4383603003 hasConceptScore W4383603003C84525736 @default.
• W4383603003 hasConceptScore W4383603003C89623803 @default.
• W4383603003 hasConceptScore W4383603003C9417928 @default.
• W4383603003 hasLocation W43836030031 @default.

• next