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Automated Covid 19 cases recognition from the lung x-ray images using deep learning techniques and K-fold Cross-validation

Automated Covid 19 cases recognition from the lung x-ray images using deep learning techniques and K-fold Cross-validation AgLmN
Abstract: The Covid-19 pandemic has spread frighteningly and rapidly in all countries of the world, forcing humanity to lead an abnormal life, and the matter has been further complicated by the emergence of new strains of this epidemic, which appear to be more infectious and possibly more dangerous. The lack of full control over this epidemic and its new strains and the challenge of developing fully effective vaccinations endangers human life. Fighting against the epidemic has become important, as all these measures could not be taken in the near future. For this reason, it is important to find out whether a person infected with the virus expressed in thousands of people has Covid-19 or not and to take the necessary measures. For this purpose, in this study, we propose an artificial intelligence-based method has that will speed up the diagnosis of the pandemic by saving labour and expense. In the study, X-Ray images were processed with the most up-to-date deep image processing techniques, and an objective decision support system was created, independent of the doctors’ expertise. Our proposed system can classify x-ray images as Normal, Covid -19, and Viral Pneumonia using pre-trained deep learning networks (AlexNet, MobileNetV2, GoogleNet, ResNet8, and ResNet50) with transfer learning (TL) and K-fold validation (KFCV). The overall accuracies of the networks (AlexNet, MobileNetV2, GoogleNet, ResNet8, and ResNet50) were 98%, 97. 4%, 97. 4%, 97. 4%, 98. 5% respectively. Also, for the average performance metrics values (Sensitivity, Specificity, Precision, F1-score, and Negative predictive value(NPV)). The best results were recorded by ResNet 50+TL+KFCV (97. 9% Sensitivity, 98. 9% Specificity, 97. 9% Precision, 97. 9 % F1-Score, and 98. 9% NPV ). It is easy to diagnose in the advanced stages of the disease. As with most diseases, early diagnosis is important in COVID-19. With the proposed system based on deep learning, a very useful tool in combating the pandemic has been created by determining the disease at an early stage. The proposed system can also be used in areas with a shortage of health personnel such as rural and remote areas.
Abstract:

The Covid-19 pandemic has spread
frighteningly
and
rapidly
in all countries of the world, forcing humanity to lead an abnormal life, and the matter has been
further
complicated by the emergence of new strains of this epidemic, which appear to be more infectious and
possibly
more
This phrase/word is too simple and can be replaced with:
dangerous
. The lack of full control over this epidemic and its new strains and the challenge of developing
fully
effective vaccinations endangers human life. Fighting against the epidemic has become
This phrase/word is too simple and can be replaced with:
important
, as all these measures could not
be taken
in the near future.
For this reason
, it is
This phrase/word is too simple and can be replaced with:
important
to find out whether a person infected with the virus expressed in thousands of
This phrase/word (depending on the context) can be potentially replaced with :
people
has Covid-19 or not and to take the necessary measures.

For this purpose, in this study, we propose an artificial intelligence-based method has that will
This phrase/word (depending on the context) can be potentially replaced with :
speed up
the diagnosis of the pandemic by saving
Possible spelling mistake. 'labour' is British English.
labour
and expense. In the study, X-Ray images
were processed
with the most up-to-date deep image processing techniques, and an objective decision support
You use this word too often, consider replacing it with
system
was created
, independent of the doctors’ expertise. Our proposed
You use this word too often, consider replacing it with
system
can classify x-ray images as Normal,
Did you mean "COVID-19" or the alternative spelling "Covid-19" (= coronavirus)?
Covid
-19, and Viral Pneumonia using pre-trained deep learning networks (
Possible spelling mistake found.
AlexNet
, MobileNetV2,
Possible spelling mistake found.
GoogleNet
, ResNet8, and ResNet50) with transfer learning (TL) and K-fold validation (
Possible spelling mistake found.
KFCV
). The
overall
accuracies of the networks (
Possible spelling mistake found.
AlexNet
, MobileNetV2,
Possible spelling mistake found.
GoogleNet
, ResNet8, and ResNet50) were 98%, 97. 4%, 97. 4%, 97.
Three successive sentences begin with the same word. Consider rewording the sentence or use a thesaurus to find a synonym.
4%
, 98. 5%
respectively
.

Also
, for the average performance metrics values (Sensitivity, Specificity, Precision, F1-score, and
This phrase/word is too simple and can be replaced with:
Negative
predictive value(NPV)). The best results
were recorded
by
Possible spelling mistake found.
ResNet
50+TL+
Possible spelling mistake found.
KFCV
(97. 9% Sensitivity, 98. 9% Specificity, 97.
Three successive sentences begin with the same word. Consider rewording the sentence or use a thesaurus to find a synonym.
9%
Precision, 97. 9 % F1-Score, and 98. 9% NPV
Don't put a space before the closing parenthesis
)
.

It is easy to diagnose in the advanced stages of the disease. As with most diseases, early diagnosis is
This phrase/word is too simple and can be replaced with:
important
in COVID-19. With the proposed
You use this word too often, consider replacing it with
system
based on deep learning, a
This phrase/word is too simple and can be replaced with:
very
useful tool in combating the pandemic has
been created
by determining the disease at an early stage. The proposed
You use this word too often, consider replacing it with
system
can
also
be
This phrase/word (depending on the context) can be potentially replaced with :
used
in areas with a shortage of health personnel such as rural and remote areas.
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IELTS academic Automated Covid 19 cases recognition from the lung x-ray images using deep learning techniques and K-fold Cross-validation

Academic
  American English
5 paragraphs
343 words
5.5
Overall Band Score
Coherence and Cohesion: 5.5
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    Vary your linking phrases using synonyms
Lexical Resource: 5.5
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Grammatical Range: 6.5
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Task Achievement: 5.0
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    Present relevant ideas
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