TY - GEN
T1 - Chest x-ray classification using transfer learning on multi-GPU
AU - Ponomaryov, Volodymyr I.
AU - Almaraz-Damian, Jose A.
AU - Reyes-Reyes, Rogelio
AU - Cruz-Ramos, Clara
N1 - Publisher Copyright:
© COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.
PY - 2021
Y1 - 2021
N2 - Since the first quarter of this year, the spread of SARS-CoV-19 virus has been a worldwide health priority. Medical testing consists of Lab studies, PCR tests, CT, PET, which are time-consuming, some countries lack these resources. One medical tool for diagnosis is X-Ray imaging, which is one of the fastest and low-cost resources for physicians to detect and to distinguish among these different diseases. We propose an X-Ray CAD system based on DCNN, using well-known architectures such as DenseNet-201, ResNet-50 and EfficientNet. These architectures are pre-trained on data from Imagenet classification challenge, moreover, using Transfer Learning methods to Fine-Tune the classification stage. The system is capable to visualize the learned recognition patterns applying the GRAD-CAM algorithm aiming to help physicians in seeking hidden features from perceptual vision. The proposed CAD can differentiate between COVID-19, Pneumonia, Nodules and Normal lung X-Ray images.
AB - Since the first quarter of this year, the spread of SARS-CoV-19 virus has been a worldwide health priority. Medical testing consists of Lab studies, PCR tests, CT, PET, which are time-consuming, some countries lack these resources. One medical tool for diagnosis is X-Ray imaging, which is one of the fastest and low-cost resources for physicians to detect and to distinguish among these different diseases. We propose an X-Ray CAD system based on DCNN, using well-known architectures such as DenseNet-201, ResNet-50 and EfficientNet. These architectures are pre-trained on data from Imagenet classification challenge, moreover, using Transfer Learning methods to Fine-Tune the classification stage. The system is capable to visualize the learned recognition patterns applying the GRAD-CAM algorithm aiming to help physicians in seeking hidden features from perceptual vision. The proposed CAD can differentiate between COVID-19, Pneumonia, Nodules and Normal lung X-Ray images.
KW - CNN
KW - COVID-19
KW - Classification
KW - Deep Learning
KW - Multi-GPU
KW - X-Ray
UR - http://www.scopus.com/inward/record.url?scp=85109142336&partnerID=8YFLogxK
U2 - 10.1117/12.2587537
DO - 10.1117/12.2587537
M3 - Contribución a la conferencia
AN - SCOPUS:85109142336
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - Real-Time Image Processing and Deep Learning 2021
A2 - Kehtarnavaz, Nasser
A2 - Carlsohn, Matthias F.
PB - SPIE
T2 - Real-Time Image Processing and Deep Learning 2021
Y2 - 12 April 2021 through 16 April 2021
ER -