A Multi-Stage Hybrid Learning Model with Advanced Feature Fusion for Enhanced Prostate Cancer Classification
Sameh Abd El-Ghany, A A Abd El-Aziz, Dechang Chen · 2025
Read A Multi-Stage Hybrid Learning Model with Advanced Feature Fusion for Enhanced Prostate Cancer Classification by Sameh Abd El-Ghany, A A Abd El-Aziz, Dechang Chen (2025) 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-12731423.txt.
Description
Sameh Abd El-Ghany and AA Abd El-Aziz build a multi stage hybrid learning model that sorts prostate cancer in MRI scans by fusing deep and hand made features. This cancer is the second most common and deadly in men, and tissue looks vary widely. The study came out in twenty twenty-five.
Sharp sorting needs both deep meaning features from convolutional networks and hand built texture marks like gradient histograms. The frame joins those two streams across stages. It also tests ways to shrink feature sets and to classify, hunting for a tougher, more telling mix. The aim is better computer aided diagnosis from scans.
Catalog record
| CREATOR | Sameh Abd El-Ghany, A A Abd El-Aziz, Dechang Chen |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 113 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |