Anatomical-Contextual YOLOv8–YOLOv12 Framework for Pulmonary Nodule Detection in CT: Multi-Organ Learning and Cross-Dataset Validation
Daniel Resendiz-Ventura, Moisés Márquez-Olivera, Viridiana Hernández-Herrera, Laura Marrujo-García, Antonio-Gustavo Juárez-Gracia, Irlanda Pacheco-Bravo · 2026
Read Anatomical-Contextual YOLOv8–YOLOv12 Framework for Pulmonary Nodule Detection in CT: Multi-Organ Learning and Cross-Dataset Validation by Daniel Resendiz-Ventura, Moisés Márquez-Olivera, Viridiana Hernández-Herrera, Laura Marrujo-García, Antonio-Gustavo Juárez-Gracia, Irlanda Pacheco-Bravo (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-13605659.txt.
Description
Daniel Resendiz-Ventura leads a team on a YOLO frame for lung nodule finds. Their study from twenty twenty-six uses CT scans of the chest. Early lung cancer finds hinge on sharp nodule detection.
Their frame trains on nodules plus nearby body structures. Those structures act as spatial guides during training. The nodule stays the sole disease class in the model. Models were trained on one data set of many slices. They were then tested on a second set sans retraining. A cut test compared context and no context forms. Context gave steady gains across all model types.
Catalog record
| CREATOR | Daniel Resendiz-Ventura, Moisés Márquez-Olivera, Viridiana Hernández-Herrera, Laura Marrujo-García, Antonio-Gustavo Juárez-Gracia, Irlanda Pacheco-Bravo |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 87 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |