Automated DFU detection through GA-selected CNN ensemble with Grad-CAM interpretability
Shreya Girotra, Achin Jain, Sarita Yadav, Arvind Panwar, Harsh Taneja, Lukas Herout, Ondrej Krejcar, Hamidreza Namazi · 2025
Read Automated DFU detection through GA-selected CNN ensemble with Grad-CAM interpretability by Shreya Girotra, Achin Jain, Sarita Yadav, Arvind Panwar, Harsh Taneja, Lukas Herout, Ondrej Krejcar, Hamidreza Namazi (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-12780072.txt.
Description
Shreya Girotra and co-authors automate detection of diabetic foot ulcers in a 2025 paper. These ulcers are a dangerous, sometimes fatal complication of diabetes, often ending in lower limb amputation. Deep learning can analyze medical images for them, but models need both accuracy and interpretability before clinicians trust them.
The authors build an ensemble of convolutional networks, selected by a genetic algorithm to combine the strongest performers. A visualization method, Grad-CAM, shows which image regions drive each decision. An ensemble chosen by evolution, with its reasoning visible, fits clinical screening workflows.
Catalog record
| CREATOR | Shreya Girotra, Achin Jain, Sarita Yadav, Arvind Panwar, Harsh Taneja, Lukas Herout, Ondrej Krejcar, Hamidreza Namazi |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 124 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |