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231226s2023 xx |||||o 00| ||eng c |
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7 |
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|a 10.1016/j.eswa.2022.118576
|2 doi
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| 028 |
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2 |
|a pubmed24n1528.xml
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|a DE-627
|b ger
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|e rakwb
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| 041 |
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|a eng
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| 100 |
1 |
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|a Nahiduzzaman, Md
|e verfasserin
|4 aut
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| 245 |
1 |
0 |
|a ChestX-Ray6
|b Prediction of multiple diseases including COVID-19 from chest X-ray images using convolutional neural network
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| 264 |
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|c 2023
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| 336 |
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|a Text
|b txt
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|a ƒaComputermedien
|b c
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|a ƒa Online-Ressource
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|a Date Revised 09.09.2024
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a © 2022 Elsevier Ltd. All rights reserved.
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|a In the last few decades, several epidemic diseases have been introduced. In some cases, doctors and medical physicians are facing difficulties in identifying these diseases correctly. A machine can perform some of these identification tasks more accurately than a human if it is trained correctly. With time, the number of medical data is increasing. A machine can analyze this medical data and extract knowledge from this data, which can help doctors and medical physicians. This study proposed a lightweight convolutional neural network (CNN) named ChestX-ray6 that automatically detects pneumonia, COVID19, cardiomegaly, lung opacity, and pleural from digital chest x-ray images. Here multiple databases have been combined, containing 9,514 chest x-ray images of normal and other five diseases. The lightweight ChestX-ray6 model achieved an accuracy of 80% for the detection of six diseases. The ChestX-ray6 model has been saved and used for binary classification of normal and pneumonia patients to reveal the model's generalization power. The pre-trained ChestX-ray6 model has achieved an accuracy and recall of 97.94% and 98% for binary classification, which outweighs the state-of-the-art (SOTA) models
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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 Cardiomegaly
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| 650 |
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4 |
|a ChestX-Ray6
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| 650 |
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4 |
|a Convolutional neural network (CNN)
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| 650 |
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4 |
|a DenseNet121
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| 650 |
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4 |
|a Lung opacity
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| 650 |
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4 |
|a MobileNetV2
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| 650 |
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4 |
|a Pleural
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| 650 |
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4 |
|a Pneumonia
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| 650 |
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4 |
|a ResNet50
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| 650 |
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4 |
|a VGG19
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| 700 |
1 |
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|a Islam, Md Rabiul
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Hassan, Rakibul
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t Expert systems with applications
|d 1999
|g 211(2023) vom: 01. Jan., Seite 118576
|w (DE-627)NLM098196782
|x 0957-4174
|7 nnns
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| 773 |
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|g volume:211
|g year:2023
|g day:01
|g month:01
|g pages:118576
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|u http://dx.doi.org/10.1016/j.eswa.2022.118576
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