Résumé: Chest X-ray (CXR) is a prominent and cost-effective medical imaging tool used in healthcare sectors. Coronavirus (COVID-19) has recently proliferated around the world, and different waves are returning, posing a serious threat to the global economy and health. Deep learning networks are being used to detect infected patients from CXR images, which is a huge step in making a prompt COVID-19 pre-diagnosis and reducing the workload of medical staff. However, in CXR images, there are complexity in structure and patterns of gray-scale distribution, as well as complex hemidiaphragm borders and details that might interfere with the machine 's and doctor's diagnosis. In particular, this paper developed a new multi-scale parallel CNN (called MSP-CNN) for denoising CXR images and a distinctive COVID-19 application that can improve image quality. The foundation of MSP-CNN is comprised of several fundamental parts are: (a) Extracting spatial information with a multi-scale feature extractor (b) Efficient Spatial Channel Attention(ESCA) without raising the network parameters, more attention to be paid to complex structures and hemidiaphragm edges in CXR images. Extensive testing has shown that our MSP-CNN is better at preserving complex structures of features in CXR images. Rigorous experiments also show that MSP-CNN has a positive influence on CXR image classification and COVID-19 case detection from denoised CXR images. It is envisioned that with reliable accuracy, this method can be introduced for clinical practices in the future.

Langue: Anglais