TY - GEN
T1 - Covid-19 detection from X-ray images using Customized Convolutional Neural Network
AU - Shafiq, Shahzad
AU - Ali, Luqman
AU - Khan, Wasif
AU - Ullah, Rooh
AU - Khan, Tanveer Ahmed
AU - Alnaiiar, Fady
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - COVID-19 continues to have a devastating impact on the lives of people all over the world. Various new technologies arose in the research environment to assist mankind in surviving and living a better life. It is important to screen the infected patients in a timely and cost-effective manner to combat this disease and avoid its transmission. To achieve this aim, detection of Covid-19 from radiological evaluation of chest x-ray images using deep learning algorithms is less expensive and easily available option as it ensures fast and efficient diagnosis of the disease. Therefore, this paper presents a novel customized convolutional neural network (CNN) approach for the detection of COVID-19 from chest x-ray images. The performance of the proposed model is evaluated on three different size datasets, created from publicly available datasets. Experimental results show that the proposed model has better performance on Dataset 2. A very large increase or decrease of the number of samples in the dataset degrades the performance of the proposed model. The performance of the CNN model is compared with traditional pretrained networks namely VGG-16, VGG-19, ResNet-50 and Inception-V3. All the models show promising performance on dataset 2 which shows that optimum amount of data is enough for the model to lean features from the input data. Overall, the best validation accuracy of 97.78 was achieved by the proposed model on dataset 2.
AB - COVID-19 continues to have a devastating impact on the lives of people all over the world. Various new technologies arose in the research environment to assist mankind in surviving and living a better life. It is important to screen the infected patients in a timely and cost-effective manner to combat this disease and avoid its transmission. To achieve this aim, detection of Covid-19 from radiological evaluation of chest x-ray images using deep learning algorithms is less expensive and easily available option as it ensures fast and efficient diagnosis of the disease. Therefore, this paper presents a novel customized convolutional neural network (CNN) approach for the detection of COVID-19 from chest x-ray images. The performance of the proposed model is evaluated on three different size datasets, created from publicly available datasets. Experimental results show that the proposed model has better performance on Dataset 2. A very large increase or decrease of the number of samples in the dataset degrades the performance of the proposed model. The performance of the CNN model is compared with traditional pretrained networks namely VGG-16, VGG-19, ResNet-50 and Inception-V3. All the models show promising performance on dataset 2 which shows that optimum amount of data is enough for the model to lean features from the input data. Overall, the best validation accuracy of 97.78 was achieved by the proposed model on dataset 2.
KW - Convolutional Neural Network
KW - Covid-19
KW - Deep Learning
KW - Pandemic
KW - SARs-2
KW - computer vision
UR - http://www.scopus.com/inward/record.url?scp=85130871946&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85130871946&partnerID=8YFLogxK
U2 - 10.1109/ICAI55435.2022.9773586
DO - 10.1109/ICAI55435.2022.9773586
M3 - Conference contribution
AN - SCOPUS:85130871946
T3 - 2nd IEEE International Conference on Artificial Intelligence, ICAI 2022
SP - 7
EP - 12
BT - 2nd IEEE International Conference on Artificial Intelligence, ICAI 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd IEEE International Conference on Artificial Intelligence, ICAI 2022
Y2 - 30 March 2022 through 31 March 2022
ER -