Exploring the efficacy of various CNN architectures in diagnosing oral cancer from squamous cell carcinoma
Deep learning models, particularly MobileNetV2, achieved high classification accuracy (95.41 %) for OCSCC.CNN-based methods show promise for early-stage OCSCC and oral dysplasia diagnosis. Performance parameters like precision, recall, and F1-score help optimize CNN model selection for this task.
Authors
- Ravindra H. Patil (ORCID: https://orcid.org/0000-0003-2731-3413)
- Gagandeep Kaur (ORCID: https://orcid.org/0000-0002-2674-9947)
- Gitanjali R. Shinde (ORCID: https://orcid.org/0000-0001-6623-8722)
- Pandurang M. Kulkarni (ORCID: https://orcid.org/0000-0003-1212-8573)
- Nidhi Sarwe
- Abhishek Pingale
- Yash Sarolkar
Institutions
- International Institute of Information Technology (IN)
- Symbiosis International University (IN)
Publication Details
- Journal
- MethodsX
- Published
- 2024-11-05
- DOI
- https://doi.org/10.1016/j.mex.2024.103034
- Citations
- 11
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 3.82