Hybrid Fusion of Time–Intensity Curve, Deep Learning, and Fractional Zernike–Caputo Features for Accurate Liver Lesion Classification in DCE-MRI
Ali M. Hasan, Noor K. N. Al-Waely, Wallaa L. Alfalluji, Rabha W. Ibrahim, Hamid A. Jalab, Farid Meziane · 2026
Read Hybrid Fusion of Time–Intensity Curve, Deep Learning, and Fractional Zernike–Caputo Features for Accurate Liver Lesion Classification in DCE-MRI by Ali M. Hasan, Noor K. N. Al-Waely, Wallaa L. Alfalluji, Rabha W. Ibrahim, Hamid A. Jalab, Farid Meziane (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/pmc-13608649.txt.
Description
Ali Hasan and co-authors published this imaging study in 2026 on sorting liver lumps as harmless or cancerous. Contrast MRI films show how a lump lights up over time. Yet many lumps share the same pattern, which fools readers. The team built a hybrid system that fuses three views of each lump. One is the curve of its brightening over time. Others are deep learning traits from the image, and math traits that code shape and texture in a fractional form.
Fused, the three views gave each lump a rich signature. On 645 dynamic MRI cases, the system hit 94.05 percent accuracy, beating single-view rivals. The authors say the blend cuts the doubt that haunts liver reads. They offer it as a support tool for doctors, not a stand-in, and call for wider clinic tests.
Catalog record
| CREATOR | Ali M. Hasan, Noor K. N. Al-Waely, Wallaa L. Alfalluji, Rabha W. Ibrahim, Hamid A. Jalab, Farid Meziane |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 68 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |