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231226s2022 xx |||||o 00| ||eng c |
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|a 10.1007/s11227-022-04631-z
|2 doi
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|a eng
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| 100 |
1 |
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|a Ullah, Farhan
|e verfasserin
|4 aut
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| 245 |
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|a Explainable artificial intelligence approach in combating real-time surveillance of COVID19 pandemic from CT scan and X-ray images using ensemble model
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|c 2022
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|a Text
|b txt
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Revised 05.09.2024
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022.
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|a Population size has made disease monitoring a major concern in the healthcare system, due to which auto-detection has become a top priority. Intelligent disease detection frameworks enable doctors to recognize illnesses, provide stable and accurate results, and lower mortality rates. An acute and severe disease known as Coronavirus (COVID19) has suddenly become a global health crisis. The fastest way to avoid the spreading of Covid19 is to implement an automated detection approach. In this study, an explainable COVID19 detection in CT scan and chest X-ray is established using a combination of deep learning and machine learning classification algorithms. A Convolutional Neural Network (CNN) collects deep features from collected images, and these features are then fed into a machine learning ensemble for COVID19 assessment. To identify COVID19 disease from images, an ensemble model is developed which includes, Gaussian Naive Bayes (GNB), Support Vector Machine (SVM), Decision Tree (DT), Logistic Regression (LR), K-Nearest Neighbor (KNN), and Random Forest (RF). The overall performance of the proposed method is interpreted using Gradient-weighted Class Activation Mapping (Grad-CAM), and t-distributed Stochastic Neighbor Embedding (t-SNE). The proposed method is evaluated using two datasets containing 1,646 and 2,481 CT scan images gathered from COVID19 patients, respectively. Various performance comparisons with state-of-the-art approaches were also shown. The proposed approach beats existing models, with scores of 98.5% accuracy, 99% precision, and 99% recall, respectively. Further, the t-SNE and explainable Artificial Intelligence (AI) experiments are conducted to validate the proposed approach
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| 650 |
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4 |
|a Journal Article
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| 650 |
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|a COVID19
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| 650 |
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4 |
|a Covid Severity
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| 650 |
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|a Ensemble learning
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| 650 |
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4 |
|a Explainable AI
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| 650 |
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4 |
|a Features selection
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| 650 |
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4 |
|a IoT
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| 700 |
1 |
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|a Moon, Jihoon
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Naeem, Hamad
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Jabbar, Sohail
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t The Journal of supercomputing
|d 1998
|g 78(2022), 17 vom: 22., Seite 19246-19271
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|x 0920-8542
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|g volume:78
|g year:2022
|g number:17
|g day:22
|g pages:19246-19271
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|u http://dx.doi.org/10.1007/s11227-022-04631-z
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