Chest x-ray classification using transfer learning on multi-GPU

Volodymyr I. Ponomaryov; Jose A. Almaraz-Damian; Rogelio Reyes-Reyes; Clara Cruz-Ramos

doi:10.1117/12.2587537

12 April 2021 Chest x-ray classification using transfer learning on multi-GPU

Volodymyr I. Ponomaryov, Jose A. Almaraz-Damian, Rogelio Reyes-Reyes, Clara Cruz-Ramos

Proceedings Volume 11736, Real-Time Image Processing and Deep Learning 2021; 117360H (2021) https://doi.org/10.1117/12.2587537
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

Abstract

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.

Conference Presentation

Citation Download Citation

Volodymyr I. Ponomaryov, Jose A. Almaraz-Damian, Rogelio Reyes-Reyes, and Clara Cruz-Ramos "Chest x-ray classification using transfer learning on multi-GPU", Proc. SPIE 11736, Real-Time Image Processing and Deep Learning 2021, 117360H (12 April 2021); https://doi.org/10.1117/12.2587537

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