TY - GEN
T1 - Covid-19 Detection from Chest X-Ray Images Using Deep Learning Techniques
AU - Mahadeva, Rajesh
AU - Soni, Piyush
AU - Chaurasia, Vijayshri
AU - Kureel, Sunil
AU - Patel, Vivek
AU - Sharma, Sonu
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - This work proposes the Covid-DWNet deep learning-based architecture for the quick identification of Covid-19 and other symptoms from chest CT and X-ray images. Depth-wise dilated convolutions (DDC) units and feature reuse residual block (FRB) units form the foundation of the architecture, which effectively extracts a variety of features from the chest scan pictures. The proposed architecture greatly enhances the ability of CT images and X-ray images to recognize Covid-19 and other pulmonary diseases. In addition, Skip connections were introduced from the first Feature Residual Block layer to the last Feature Residual Block layer for the retention of features in the tensors. An accuracy of 98.44% is achieved on X-ray images as compared to 96.8% in the Covid-DWNet architecture. In addition, Skip connections were introduced from the first Feature Residual Block layer to the last Feature Residual Block layer for the retention of features in the tensors. An accuracy of 98.44% is achieved on X-ray images as compared to 96.8% in the Covid-DWNet architecture.
AB - This work proposes the Covid-DWNet deep learning-based architecture for the quick identification of Covid-19 and other symptoms from chest CT and X-ray images. Depth-wise dilated convolutions (DDC) units and feature reuse residual block (FRB) units form the foundation of the architecture, which effectively extracts a variety of features from the chest scan pictures. The proposed architecture greatly enhances the ability of CT images and X-ray images to recognize Covid-19 and other pulmonary diseases. In addition, Skip connections were introduced from the first Feature Residual Block layer to the last Feature Residual Block layer for the retention of features in the tensors. An accuracy of 98.44% is achieved on X-ray images as compared to 96.8% in the Covid-DWNet architecture. In addition, Skip connections were introduced from the first Feature Residual Block layer to the last Feature Residual Block layer for the retention of features in the tensors. An accuracy of 98.44% is achieved on X-ray images as compared to 96.8% in the Covid-DWNet architecture.
UR - https://www.scopus.com/pages/publications/105004560105
UR - https://www.scopus.com/pages/publications/105004560105#tab=citedBy
U2 - 10.1109/IHCSP63227.2024.10959818
DO - 10.1109/IHCSP63227.2024.10959818
M3 - Conference contribution
AN - SCOPUS:105004560105
T3 - 2nd IEEE International Conference on Innovations in High-Speed Communication and Signal Processing, IHCSP 2024
BT - 2nd IEEE International Conference on Innovations in High-Speed Communication and Signal Processing, IHCSP 2024
A2 - Kumre, Laxmi
A2 - Chaurasia, Vijayshri
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd IEEE International Conference on Innovations in High-Speed Communication and Signal Processing, IHCSP 2024
Y2 - 6 December 2024 through 8 December 2024
ER -