Diagnosis of odontogenic keratocysts and non-keratocysts using edge attention convolution neural network
The suggested method will work for OKC and Non-KC diagnosis automation systems. The use of a whole slide imaging scanner has the potential to increase accuracy and remove human bias.
Authors
- Giuseppe Minervini (ORCID: https://orcid.org/0000-0002-8309-1272)
- Roopa S. Rao (ORCID: https://orcid.org/0000-0001-7000-6609)
- Marco Cicciù (ORCID: https://orcid.org/0000-0003-2311-9728)
- Vincenzo Ronsivalle (ORCID: https://orcid.org/0000-0003-3059-4981)
- Nivedan Yakolli (ORCID: https://orcid.org/0000-0001-8658-2424)
- Shankargouda Patil
- Divya Biligere Shivanna
Institutions
- Roseman University of Health Sciences (US)
- M S Ramaiah University of Applied Sciences (IN)
- University of Catania (IT)
- Saveetha University (IN)
Publication Details
- Journal
- Minerva Dental and Oral Science
- Published
- 2024-09-27
- DOI
- https://doi.org/10.23736/s2724-6329.24.04874-5
- Citations
- 3
- Primary Topic
- Dental Radiography and Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.48