TY - GEN
T1 - Reinforced Cascading Convolutional Neural Networks and Vision Transformer for Lung Disease Diagnosis
AU - Akhyar, Fityanul
AU - Novamizanti, Ledya
AU - Maulana, Raihan Arfi
AU - Lung, Chi Wen
AU - Lin, Chih Yang
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Lung diseases are among the most deadly infectious diseases worldwide. Covid-19 infection is a current disease that falls within this category and has impacted public health in countries across the globe. Accordingly, this study focuses on building a lung disease identification system using a state-of-the-art deep cascade learning classification model, EfficientNet-Vision Transformer. The proposed Real ESRGAN is utilized to enhance the input of EfficientNet, while image Relative Position Encoding (iRPE) is added to improve the attention of the transformer network. Moreover, weight balancing is applied to stabilize the performance of the proposed system. When trained on the X-Ray dataset, our model achieved 93.757% accuracy on five classes of lung disease: Normal, Covid-19, Viral Pneumonia, Bacterial Pneumonia, and Tuberculosis.
AB - Lung diseases are among the most deadly infectious diseases worldwide. Covid-19 infection is a current disease that falls within this category and has impacted public health in countries across the globe. Accordingly, this study focuses on building a lung disease identification system using a state-of-the-art deep cascade learning classification model, EfficientNet-Vision Transformer. The proposed Real ESRGAN is utilized to enhance the input of EfficientNet, while image Relative Position Encoding (iRPE) is added to improve the attention of the transformer network. Moreover, weight balancing is applied to stabilize the performance of the proposed system. When trained on the X-Ray dataset, our model achieved 93.757% accuracy on five classes of lung disease: Normal, Covid-19, Viral Pneumonia, Bacterial Pneumonia, and Tuberculosis.
UR - http://www.scopus.com/inward/record.url?scp=85138751478&partnerID=8YFLogxK
U2 - 10.1109/ICCE-Taiwan55306.2022.9869132
DO - 10.1109/ICCE-Taiwan55306.2022.9869132
M3 - 會議論文篇章
AN - SCOPUS:85138751478
T3 - Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022
SP - 201
EP - 202
BT - Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022
Y2 - 6 July 2022 through 8 July 2022
ER -
