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Published Paper Details:
Paper Title
Hybrid Intelligence for Gender Classification of Dental Images: A Comparative Study of Neural-Fuzzy Systems
Authors
Payal Bhansali, Vibha Patel
Keywords
eural networks, fuzzy logic, dental radiography, gender classification.
Abstract
Hybrid neural-fuzzy systems are used for gender estimation of dental
panoramic radiographs, coupling methods such as convolutional neural net-
works (CNNs), fuzzy logic, and contrast-limited adaptive histogram equaliza-
tion. Stratified cross-validation is performed for analysis after pre-processing
on a dataset comprising 947 images. The most successful approach of SVM
augmented using expanded fuzzy rules comes with accuracy rating of 94.93%
and finds a better success than traditional models. Results highlight the mer-
its of hybrid strategies in maintaining accuracy and interpretability in dental
diagnostics and forensic purposes.
IJCRT's Publication Details
Unique Identification Number - IJCRT2504380
Paper ID - 281746
Page Number(s) - d241-d258
Pubished in - Volume 13 | Issue 4 | April 2025
DOI (Digital Object Identifier) - http://doi.one/10.1729/Journal.44625
Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882
E-ISSN Number - 2320-2882
Cite this article
Payal Bhansali, Vibha Patel, "Hybrid Intelligence for Gender Classification of Dental Images: A Comparative Study of Neural-Fuzzy Systems", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.13, Issue 4, pp.d241-d258, April 2025, Available at :http://www.ijcrt.org/papers/IJCRT2504380.pdf
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ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
ISSN and 7.97 Impact Factor Details
ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
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