"Anam-Net: Anamorphic Depth Embedding-Based Lightweight CNN for ..."

Naveen Paluru et al. (2021)

> Home

Details and statistics

DOI: 10.1109/TNNLS.2021.3054746

access: closed

type: Journal Article

metadata version: 2023-03-21

- view
  - electronic edition via DOI
  - unpaywalled version
  authority control:
- export record
  dblp key:
  - journals/tnn/PaluruDJSCPY21
- ask others
- share record
  persistent URL:
  - https://dblp.org/rec/journals/tnn/PaluruDJSCPY21
Naveen Paluru, Aveen Dayal, Håvard Bjørke Jenssen, Tomas Sakinis, Linga Reddy Cenkeramaddi, Jaya Prakash, Phaneendra K. Yalavarthy:
Anam-Net: Anamorphic Depth Embedding-Based Lightweight CNN for Segmentation of Anomalies in COVID-19 Chest CT Images. IEEE Trans. Neural Networks Learn. Syst. 32(3): 932-946 (2021)

manage site settings

To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.