Since its outbreak, COVID-19, a brand-new viral disease that causes severe pneumonia-related respiratory failure, has been sweeping the globe. The Community-Acquired Pneumonia (CAP), Normal, and COVID-19 Computed Tomography (CT) scan pictures are identified using the Involution Receptive Field Network from the Large COVID-19 CT scan slice dataset. N-pair contrastive loss is included during the network's training in order to improve the embedding representation for CT scan slices in the latent dimension. In order to enhance feature map extraction, the lightweight Involution Receptive Field Network-Medium (InRFNet-M) is presented. It is both channel-neutral and spatially specific. The assessment findings for the InRFNet-M model show a high level of validation accuracy (99 percent). The suggested InRFNet-M: Involution Receptive Field Network-Medium has shown effective classification with good accuracy and recall scores.
M. Dhruv,
CKM VIGIL Pvt Ltd, Hyderabad, Telangana, India.
R. Sai Chandra Teja,
CKM VIGIL Pvt Ltd, Hyderabad, Telangana, India.
R. Sri Devi,
Department of Anesthesiology, Sri Venkateswara Institute of Medical Sciences SVIMS Tirupati, Chittor District, Andhra Pradesh, India.
S. Nagesh Kumar,
Department of Surgical Oncology, Sri Venkateswara Institute of Medical Sciences SVIMS Tirupati, Chittor District, Andhra Pradesh, India.
Please see the link here: https://stm.bookpi.org/NTPSR-V6/article/view/7209
No comments:
Post a Comment