A Comparative Study of Decoders for Liver and Tumor Segmentation Using a Self-ONN-Based Cascaded Framework
Sidra Gul, Muhammad Salman Khan, Md Sakib Abrar Hossain, Muhammad E. H. Chowdhury, Md. Shaheenur Islam Sumon · 2024
Read A Comparative Study of Decoders for Liver and Tumor Segmentation Using a Self-ONN-Based Cascaded Framework by Sidra Gul, Muhammad Salman Khan, Md Sakib Abrar Hossain, Muhammad E. H. Chowdhury, Md. Shaheenur Islam Sumon (2024) 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/pmc-11640157.txt.
Description
Sidra Gul and co-authors compare in 2024 decoder designs for outlining liver and tumors in scan images within a two-stage network frame. Hand reading of scans is slow and hard, while early liver cancer needs fast, exact outlines. A computer aid can save time and effort.
The first stage outlines the liver, and the mask then guides a second stage that outlines tumors. The core network uses a self-organizing form of neural unit. Scores for liver outlines came near ninety eight percent on a shared overlap scale. Tumor scores also passed ninety percent with one encoder choice. Other known designs were tested for a fair comparison.
Catalog record
| CREATOR | Sidra Gul, Muhammad Salman Khan, Md Sakib Abrar Hossain, Muhammad E. H. Chowdhury, Md. Shaheenur Islam Sumon |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 45 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |