BPG is committed to discovery and dissemination of knowledge
Cited by in CrossRef
For: Sinha S, Ramesh PV, Nishant P, Morya AK, Prasad R. Novel automated non-invasive detection of ocular surface squamous neoplasia using artificial intelligence. World J Methodol 2024; 14(2): 92267 [PMID: 38983656 DOI: 10.5662/wjm.v14.i2.92267]
URL: https://www.wjgnet.com/2222-0682/full/v14/i2/92267.htm
Number Citing Articles
1
Bala Weslin D, S. Sophia. Automated Blood Group Detection System Using Image Processing for Non-Invasive Antigen Feature Extraction and Classification2025 International Conference on Visual Analytics and Data Visualization (ICVADV) 2025; : 736 doi: 10.1109/ICVADV63329.2025.10961852
2
Rolika Bansal, Santosh G Honavar. Oncological principles in the management of ocular surface squamous neoplasia - A ReviewIndian Journal of Ophthalmology 2025; 73(2): 173 doi: 10.4103/IJO.IJO_2340_24
3
Farshid Ramezani, Hossein Azimi, Behrouz Delfanian, Mobina Amanollahi, Jamshid Saeidian, Ahmad Masoumi, Hossein Farrokhpour, Elias Khalili Pour, Mehdi Khodaparast. Classification of ocular surface diseases: Deep learning for distinguishing ocular surface squamous neoplasia from pterygiumGraefe's Archive for Clinical and Experimental Ophthalmology 2025;  doi: 10.1007/s00417-025-06804-x
4
Leendert Dekker, Jan F. Olivier, Klaus Von Pressentin. The critical role of primary care clinicians in the early detection of ocular surface squamous neoplasiaSouth African Family Practice 2025; 67(1) doi: 10.4102/safp.v67i1.6065