TY - JOUR
T1 - COVID-19 Semantic Pneumonia Segmentation and Classification Using Artificial Intelligence
AU - Abdulaal, Mohammed J.
AU - Mehedi, Ibrahim M.
AU - Abusorrah, Abdullah M.
AU - Aljohani, Abdulah Jeza
AU - Milyani, Ahmad H.
AU - Rana, Md Masud
AU - Mahmoud, Mohamed
N1 - Publisher Copyright:
© 2022 Mohammed J. Abdulaal et al.
PY - 2022
Y1 - 2022
N2 - Coronavirus 2019 (COVID-19) has become a pandemic. The seriousness of COVID-19 can be realized from the number of victims worldwide and large number of deaths. This paper presents an efficient deep semantic segmentation network (DeepLabv3Plus). Initially, the dynamic adaptive histogram equalization is utilized to enhance the images. Data augmentation techniques are then used to augment the enhanced images. The second stage builds a custom convolutional neural network model using several pretrained ImageNet models and compares them to repeatedly trim the best-performing models to reduce complexity and improve memory efficiency. Several experiments were done using different techniques and parameters. Furthermore, the proposed model achieved an average accuracy of 99.6% and an area under the curve of 0.996 in the COVID-19 detection. This paper will discuss how to train a customized smart convolutional neural network using various parameters on a set of chest X-rays with an accuracy of 99.6%.
AB - Coronavirus 2019 (COVID-19) has become a pandemic. The seriousness of COVID-19 can be realized from the number of victims worldwide and large number of deaths. This paper presents an efficient deep semantic segmentation network (DeepLabv3Plus). Initially, the dynamic adaptive histogram equalization is utilized to enhance the images. Data augmentation techniques are then used to augment the enhanced images. The second stage builds a custom convolutional neural network model using several pretrained ImageNet models and compares them to repeatedly trim the best-performing models to reduce complexity and improve memory efficiency. Several experiments were done using different techniques and parameters. Furthermore, the proposed model achieved an average accuracy of 99.6% and an area under the curve of 0.996 in the COVID-19 detection. This paper will discuss how to train a customized smart convolutional neural network using various parameters on a set of chest X-rays with an accuracy of 99.6%.
UR - http://www.scopus.com/inward/record.url?scp=85138935046&partnerID=8YFLogxK
U2 - 10.1155/2022/5297709
DO - 10.1155/2022/5297709
M3 - Article
C2 - 36176933
AN - SCOPUS:85138935046
SN - 1555-4309
VL - 2022
JO - Contrast Media and Molecular Imaging
JF - Contrast Media and Molecular Imaging
M1 - 5297709
ER -