TY - GEN
T1 - Introducing Novel Radon Based Transform for Disease Detection From Chest X-Ray Images
AU - Islam, Ashhadul
AU - Mohsen, Farida
AU - Shah, Zubair
AU - Belhaouari, Samir Brahim
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Medical imaging technologies, such as chest X-rays (CXR), have demonstrated their utility in predicting diseases with high accuracy using deep learning algorithms. These models are crucial for identifying critical lung conditions. Nevertheless, the challenge lies in the resemblance of disease patterns and symptoms, which may cause misdiagnoses and critical mistakes. In our research, we introduce a novel technique for feature extraction from CXR images using an advanced version of the Radon transform, named the RadEx Transform. This method, by integrating the extracted features with CXR images, significantly enhances the learning capability of the models. We focus our study on the COVID-19 radiography dataset. The results indicate that our approach of feature extraction markedly increases accuracy beyond that achieved with raw images alone, surpassing conventional techniques by significant margins in terms of x, y, and z. Our research underscores the effectiveness of augmenting RadEx features with images in elevating the accuracy of lung disease detection. This approach holds considerable promise for advancing medical image analysis and diagnostic processes, marking a significant step forward in the domain.
AB - Medical imaging technologies, such as chest X-rays (CXR), have demonstrated their utility in predicting diseases with high accuracy using deep learning algorithms. These models are crucial for identifying critical lung conditions. Nevertheless, the challenge lies in the resemblance of disease patterns and symptoms, which may cause misdiagnoses and critical mistakes. In our research, we introduce a novel technique for feature extraction from CXR images using an advanced version of the Radon transform, named the RadEx Transform. This method, by integrating the extracted features with CXR images, significantly enhances the learning capability of the models. We focus our study on the COVID-19 radiography dataset. The results indicate that our approach of feature extraction markedly increases accuracy beyond that achieved with raw images alone, surpassing conventional techniques by significant margins in terms of x, y, and z. Our research underscores the effectiveness of augmenting RadEx features with images in elevating the accuracy of lung disease detection. This approach holds considerable promise for advancing medical image analysis and diagnostic processes, marking a significant step forward in the domain.
KW - Convolutional neural networks
KW - Feature extraction
KW - Pulmonary diseases
KW - RadEx Transform
KW - Radiography
KW - Transfer learning
UR - http://www.scopus.com/inward/record.url?scp=85195888694&partnerID=8YFLogxK
U2 - 10.1109/PAIS62114.2024.10541204
DO - 10.1109/PAIS62114.2024.10541204
M3 - Conference contribution
AN - SCOPUS:85195888694
T3 - PAIS 2024 - Proceedings: 6th International Conference on Pattern Analysis and Intelligent Systems
BT - PAIS 2024 - Proceedings
A2 - Abbas, Messaoud
A2 - Derdour, Makhlouf
A2 - Bouhamed, Mohammed Mounir
A2 - Medileh, Saci
A2 - Ben Ali, Abdelkamel
A2 - Ghoualmi-Zine, Nassira
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th International Conference on Pattern Analysis and Intelligent Systems, PAIS 2024
Y2 - 24 April 2024 through 25 April 2024
ER -