TY - GEN
T1 - Differentiation between paediatric pneumonia and normal chest X-ray images using convolutional neural networks and pseudo-attention module
AU - Galindo-Ramirez, Victor H.
AU - Ponomaryov, Volodymyr
AU - Almaraz-Damian, J. A.
AU - Reyes-Reyes, Rogelio
AU - Cruz-Ramos, Clara
N1 - Publisher Copyright:
© 2022 SPIE.
PY - 2022
Y1 - 2022
N2 - The Chest X-Ray imaging as a low resource diagnosing tool that can bring sufficiently information from the thorax, helping to a specialist to find patterns with purpose to diagnose the pneumonia disease. Also, due to the simplicity to obtain these images, Chest X-Ray is the top choice against CT, US, CT, or MRI imaging in paediatric patients. In this work, we propose a novel Pseudo-attention module based on handcraft features. Generating the Region of Interest (ROI) image of the thorax, avoiding the rest of the body and eliminating the labels contained in this type of test. After obtaining the ROI image, it is evaluated with several architectures based on Convolutional Neural Networks such as DenseNET, ResNET and MobileNET. Finally, the designed system employs Grad-Cam algorithm to provide the perceptual image of the relevant features significant in the classification of Pneumonia against Normal class. The system has demonstrated similar or better performance in comparison with the state-of-the-art methods using evaluation metrics such as Accuracy, Precision, Sensibility, and F1 score.
AB - The Chest X-Ray imaging as a low resource diagnosing tool that can bring sufficiently information from the thorax, helping to a specialist to find patterns with purpose to diagnose the pneumonia disease. Also, due to the simplicity to obtain these images, Chest X-Ray is the top choice against CT, US, CT, or MRI imaging in paediatric patients. In this work, we propose a novel Pseudo-attention module based on handcraft features. Generating the Region of Interest (ROI) image of the thorax, avoiding the rest of the body and eliminating the labels contained in this type of test. After obtaining the ROI image, it is evaluated with several architectures based on Convolutional Neural Networks such as DenseNET, ResNET and MobileNET. Finally, the designed system employs Grad-Cam algorithm to provide the perceptual image of the relevant features significant in the classification of Pneumonia against Normal class. The system has demonstrated similar or better performance in comparison with the state-of-the-art methods using evaluation metrics such as Accuracy, Precision, Sensibility, and F1 score.
KW - CNN
KW - Classification
KW - Deep Learning
KW - Pneumonia
KW - Pseudo-attention
KW - X-Ray
UR - http://www.scopus.com/inward/record.url?scp=85135684993&partnerID=8YFLogxK
U2 - 10.1117/12.2618338
DO - 10.1117/12.2618338
M3 - Contribución a la conferencia
AN - SCOPUS:85135684993
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - Real-Time Image Processing and Deep Learning 2022
A2 - Kehtarnavaz, Nasser
A2 - Carlsohn, Matthias F.
PB - SPIE
T2 - Real-Time Image Processing and Deep Learning 2022
Y2 - 6 June 2022 through 12 June 2022
ER -