Tensor enhanced chest cancer classification via CNN and Vision Transformer models
Nayab Asim, Mehreen Sirshar, Mohammad Zubair Khan, Sidra Ejaz, Sobia Khalid, Ibrahim Aljubayri, Abdulrahman Alahmadi · 2026
Read Tensor enhanced chest cancer classification via CNN and Vision Transformer models by Nayab Asim, Mehreen Sirshar, Mohammad Zubair Khan, Sidra Ejaz, Sobia Khalid, Ibrahim Aljubayri, Abdulrahman Alahmadi (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/plos-pone-0348863.txt.
Description
Nayab Asim and co-authors compared image networks and a transformer model for naming chest cancers from scans in 2026. Lung cancer kills more people than any other cancer, and computer help with early, accurate reading is in high demand.
All models were fed images prepared the same tensor based way, using a public annotated data set. Classic networks including ResNet and EfficientNet reached the highest accuracy, while the transformer proved strong at grasping broad image patterns. The side by side test, on equal footing, helps hospitals judge which style of model deserves their computing budget and trust.
Catalog record
| CREATOR | Nayab Asim, Mehreen Sirshar, Mohammad Zubair Khan, Sidra Ejaz, Sobia Khalid, Ibrahim Aljubayri, Abdulrahman Alahmadi |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 44078 |
| LICENSE | CC_BY |
| RIGHTS | CC BY |
| SOURCE | PLOS |