FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4382241421> ?p ?o ?g. }

• W4382241421 abstract "In May 2022, the World Health Organization (WHO) European Region announced an atypical Monkeypox epidemic in response to reports of numerous cases in some member countries unrelated to those where the illness is endemic. This issue has raised concerns about the widespread nature of this disease around the world. The experience with Coronavirus Disease 2019 (COVID-19) has increased awareness about pandemics among researchers and health authorities.Deep Neural Networks (DNNs) have shown promising performance in detecting COVID-19 and predicting its outcomes. As a result, researchers have begun applying similar methods to detect Monkeypox disease. In this study, we utilize a dataset comprising skin images of three diseases: Monkeypox, Chickenpox, Measles, and Normal cases. We develop seven DNN models to identify Monkeypox from these images. Two scenarios of including two classes and four classes are implemented.The results show that our proposed DenseNet201-based architecture has the best performance, with Accuracy = 97.63%, F1-Score = 90.51%, and Area Under Curve (AUC) = 94.27% in two-class scenario; and Accuracy = 95.18%, F1-Score = 89.61%, AUC = 92.06% for four-class scenario. Comparing our study with previous studies with similar scenarios, shows that our proposed model demonstrates superior performance, particularly in terms of the F1-Score metric. For the sake of transparency and explainability, Local Interpretable Model-Agnostic Explanations (LIME) and Gradient-weighted Class Activation Mapping (Grad-Cam) were developed to interpret the results. These techniques aim to provide insights into the decision-making process, thereby increasing the trust of clinicians.The DenseNet201 model outperforms the other models in terms of the confusion metrics, regardless of the scenario. One significant accomplishment of this study is the utilization of LIME and Grad-Cam to identify the affected areas and assess their significance in diagnosing diseases based on skin images. By incorporating these techniques, we enhance our understanding of the infected regions and their relevance in distinguishing Monkeypox from other similar diseases. Our proposed model can serve as a valuable auxiliary tool for diagnosing Monkeypox and distinguishing it from other related conditions." @default.
• W4382241421 created "2023-06-28" @default.
• W4382241421 creator A5011099715 @default.
• W4382241421 creator A5024938379 @default.
• W4382241421 creator A5059088487 @default.
• W4382241421 creator A5069699103 @default.
• W4382241421 creator A5078420722 @default.
• W4382241421 creator A5089302640 @default.
• W4382241421 date "2023-06-27" @default.
• W4382241421 modified "2023-10-08" @default.
• W4382241421 title "Monkeypox detection using deep neural networks" @default.
• W4382241421 cites W1665930980 @default.
• W4382241421 cites W1980276147 @default.
• W4382241421 cites W2004384520 @default.
• W4382241421 cites W2088524605 @default.
• W4382241421 cites W2368027807 @default.
• W4382241421 cites W2561981131 @default.
• W4382241421 cites W2610332124 @default.
• W4382241421 cites W2955748575 @default.
• W4382241421 cites W2963446712 @default.
• W4382241421 cites W2965023912 @default.
• W4382241421 cites W3005209336 @default.
• W4382241421 cites W3081351413 @default.
• W4382241421 cites W3082965341 @default.
• W4382241421 cites W3126537794 @default.
• W4382241421 cites W3136075083 @default.
• W4382241421 cites W3209832830 @default.
• W4382241421 cites W3210820305 @default.
• W4382241421 cites W4211060053 @default.
• W4382241421 cites W4234185093 @default.
• W4382241421 cites W4283445814 @default.
• W4382241421 cites W4295036078 @default.
• W4382241421 cites W4302286670 @default.
• W4382241421 cites W4303856989 @default.
• W4382241421 cites W4303953748 @default.
• W4382241421 cites W4310635486 @default.
• W4382241421 cites W4311123089 @default.
• W4382241421 cites W4321495983 @default.
• W4382241421 cites W4323060186 @default.
• W4382241421 doi "https://doi.org/10.1186/s12879-023-08408-4" @default.
• W4382241421 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37370031" @default.
• W4382241421 hasPublicationYear "2023" @default.
• W4382241421 type Work @default.
• W4382241421 citedByCount "0" @default.
• W4382241421 crossrefType "journal-article" @default.
• W4382241421 hasAuthorship W4382241421A5011099715 @default.
• W4382241421 hasAuthorship W4382241421A5024938379 @default.
• W4382241421 hasAuthorship W4382241421A5059088487 @default.
• W4382241421 hasAuthorship W4382241421A5069699103 @default.
• W4382241421 hasAuthorship W4382241421A5078420722 @default.
• W4382241421 hasAuthorship W4382241421A5089302640 @default.
• W4382241421 hasBestOaLocation W43822414211 @default.
• W4382241421 hasConcept C104317684 @default.
• W4382241421 hasConcept C119857082 @default.
• W4382241421 hasConcept C138816342 @default.
• W4382241421 hasConcept C142724271 @default.
• W4382241421 hasConcept C144133560 @default.
• W4382241421 hasConcept C154945302 @default.
• W4382241421 hasConcept C162853370 @default.
• W4382241421 hasConcept C176217482 @default.
• W4382241421 hasConcept C2778011067 @default.
• W4382241421 hasConcept C2781356689 @default.
• W4382241421 hasConcept C40767141 @default.
• W4382241421 hasConcept C41008148 @default.
• W4382241421 hasConcept C46578552 @default.
• W4382241421 hasConcept C55493867 @default.
• W4382241421 hasConcept C71924100 @default.
• W4382241421 hasConcept C86803240 @default.
• W4382241421 hasConceptScore W4382241421C104317684 @default.
• W4382241421 hasConceptScore W4382241421C119857082 @default.
• W4382241421 hasConceptScore W4382241421C138816342 @default.
• W4382241421 hasConceptScore W4382241421C142724271 @default.
• W4382241421 hasConceptScore W4382241421C144133560 @default.
• W4382241421 hasConceptScore W4382241421C154945302 @default.
• W4382241421 hasConceptScore W4382241421C162853370 @default.
• W4382241421 hasConceptScore W4382241421C176217482 @default.
• W4382241421 hasConceptScore W4382241421C2778011067 @default.
• W4382241421 hasConceptScore W4382241421C2781356689 @default.
• W4382241421 hasConceptScore W4382241421C40767141 @default.
• W4382241421 hasConceptScore W4382241421C41008148 @default.
• W4382241421 hasConceptScore W4382241421C46578552 @default.
• W4382241421 hasConceptScore W4382241421C55493867 @default.
• W4382241421 hasConceptScore W4382241421C71924100 @default.
• W4382241421 hasConceptScore W4382241421C86803240 @default.
• W4382241421 hasIssue "1" @default.
• W4382241421 hasLocation W43822414211 @default.
• W4382241421 hasLocation W43822414212 @default.
• W4382241421 hasOpenAccess W4382241421 @default.
• W4382241421 hasPrimaryLocation W43822414211 @default.
• W4382241421 hasRelatedWork W4283644801 @default.
• W4382241421 hasRelatedWork W4289313525 @default.
• W4382241421 hasRelatedWork W4297905769 @default.
• W4382241421 hasRelatedWork W4298110654 @default.
• W4382241421 hasRelatedWork W4309073603 @default.
• W4382241421 hasRelatedWork W4309517585 @default.
• W4382241421 hasRelatedWork W4310083502 @default.
• W4382241421 hasRelatedWork W4311376888 @default.
• W4382241421 hasRelatedWork W4313363546 @default.
• W4382241421 hasRelatedWork W4319827613 @default.
• W4382241421 hasVolume "23" @default.

• next