Authors :
Ms. CH. SRILAKSHMI; Gowtham H; Jashvanth S R
Volume/Issue :
Volume 7 - 2022, Issue 12 - December
Google Scholar :
https://bit.ly/3IIfn9N
Scribd :
https://bit.ly/3I72sSe
DOI :
https://doi.org/10.5281/zenodo.7488010
Abstract :
Coronavirus Disease 2019 requires chest
computed tomography (CT) imaging data for early
diagnosis, treatment, and prognosis (COVID-19).
Artificial intelligence has been triedto assist physicians in
enhancing the diagnosisaccuracy and operating efficiency
of CT. Existing supervised techniques on CT images of
COVID- 19 pneumonia require voxel-based annotations
for training, which takes a long time and effort. This
research developed a weakly-supervised technique for
COVID-19 lesion localization based on generative
adversarial networks (GAN) using just image-level labels.
We originally presented a GAN-based framework for
generating normal- looking CT slices from CT scans with
COVID-19 lesions. We then devised a unique feature
matching technique to enhance the realism of produced
pictures by directing the generator tocapture the intricate
texture of chest CT images. Finally, by subtracting the
output picture from its matching input image, the
localization map of lesions may be simply generated. We
can increasethe classification accuracy of our diagnostic
system by adding a classifier branch to the GAN- based
architecture to identify localization maps.In this paper,
three CT datasets regarding COVID-19 were obtained for
examination from hospitals in Sao Paulo, the Italian
Society of Medical and Interventional Radiology, and
China Medical University. Our method of weakly
supervised learning yielded AUC of 0.883, dice coefficient
of 0.575, accuracy of 0.884, sensitivityof 0.647, specificity
of 0.929, and F1-score of0.640 significantly outperformed
other frequently used weakly supervised object
localization algorithms. In addition, we compared the
suggested technique to fully supervised learning methods
in the COVID-19 lesion segmentationproblem, and the
proposed weakly supervised method still produces a
competitive performance with a dice coefficient of 0.575.
Furthermore, we examined the relationship between
illness severityand visual score and discovered that the
common severity cohort had the largest sample size as
well as the highest visual score, implying that our method
can aid in the rapid diagnosis of COVID- 19 patients,
particularly in the massive common severity cohort
.Finally, we argued that this uniquetechnique may be used
as an accurate andefficient tool to remove the bottleneck
of expert annotation costs and promote the advancement
of computer-aided COVID-19 diagnosis.
Keywords :
Coronavirus Disease 2019, Generative Adversarial Network, Lesion Location, and Lesion Segmentation
Coronavirus Disease 2019 requires chest
computed tomography (CT) imaging data for early
diagnosis, treatment, and prognosis (COVID-19).
Artificial intelligence has been triedto assist physicians in
enhancing the diagnosisaccuracy and operating efficiency
of CT. Existing supervised techniques on CT images of
COVID- 19 pneumonia require voxel-based annotations
for training, which takes a long time and effort. This
research developed a weakly-supervised technique for
COVID-19 lesion localization based on generative
adversarial networks (GAN) using just image-level labels.
We originally presented a GAN-based framework for
generating normal- looking CT slices from CT scans with
COVID-19 lesions. We then devised a unique feature
matching technique to enhance the realism of produced
pictures by directing the generator tocapture the intricate
texture of chest CT images. Finally, by subtracting the
output picture from its matching input image, the
localization map of lesions may be simply generated. We
can increasethe classification accuracy of our diagnostic
system by adding a classifier branch to the GAN- based
architecture to identify localization maps.In this paper,
three CT datasets regarding COVID-19 were obtained for
examination from hospitals in Sao Paulo, the Italian
Society of Medical and Interventional Radiology, and
China Medical University. Our method of weakly
supervised learning yielded AUC of 0.883, dice coefficient
of 0.575, accuracy of 0.884, sensitivityof 0.647, specificity
of 0.929, and F1-score of0.640 significantly outperformed
other frequently used weakly supervised object
localization algorithms. In addition, we compared the
suggested technique to fully supervised learning methods
in the COVID-19 lesion segmentationproblem, and the
proposed weakly supervised method still produces a
competitive performance with a dice coefficient of 0.575.
Furthermore, we examined the relationship between
illness severityand visual score and discovered that the
common severity cohort had the largest sample size as
well as the highest visual score, implying that our method
can aid in the rapid diagnosis of COVID- 19 patients,
particularly in the massive common severity cohort
.Finally, we argued that this uniquetechnique may be used
as an accurate andefficient tool to remove the bottleneck
of expert annotation costs and promote the advancement
of computer-aided COVID-19 diagnosis.
Keywords :
Coronavirus Disease 2019, Generative Adversarial Network, Lesion Location, and Lesion Segmentation
