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Chest x-ray classification using transfer learning on multi-GPU
Real-Time Image Processing and Deep Learning 2021 ; 11736, 2021.
Article in English | Scopus | ID: covidwho-1304144
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. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: Real-Time Image Processing and Deep Learning 2021 Year: 2021 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: Real-Time Image Processing and Deep Learning 2021 Year: 2021 Document Type: Article