Leveraging deep learning and explainable AI for effective liver tumor classification from CT scan images
Meshal Alfarhood, Shatha Alotaibi, Aows Abuhaimed, Abdalrahman Alalwan · 2026
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Description
Meshal Alfarhood and co-authors build in 2026 a deep learning system that classifies liver tumors on CT scans and explains its decisions. Liver cancer is often caught late, while biopsy is invasive and expert scan reading is slow and variable. The team fine-tuned several leading image models with tailored preparation and augmentation of the scan data.
One model, EfficientNetV2, reached accuracy close to ninety-seven percent in their tests. On top of raw performance, the framework adds explanation methods that show which image regions drove a classification. That transparency is meant to build clinical trust, a gap the authors say holds back many medical image systems. The tool aims to support, not replace, radiologists facing heavy caseloads.
Catalog record
| CREATOR | Meshal Alfarhood, Shatha Alotaibi, Aows Abuhaimed, Abdalrahman Alalwan |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 77 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |