ArticleScientific reports2023
Early diagnosis of oral cancer using a hybrid arrangement of deep belief networkand combined group teaching algorithm.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed, 21 citations in OpenAlex.
- Determination of the oral carcinoma and sarcoma in contrast enhanced CT images using deep convolutional neural networks.Scientific reports · 2025Article
- Incidence trends and projections of lip and oral cavity cancer in China 1990-2021: an age-period-cohort and decomposition analysis.BMC oral health · 2025Article
- Current AI Applications and Challenges in Oral Pathology.Oral (Basel, Switzerland) · 2025Article
- Artificial Intelligence in Oral Cancer: A Comprehensive Scoping Review of Diagnostic and Prognostic Applications.Diagnostics (Basel, Switzerland) · 2025Review
- Improving laryngeal cancer detection using chaotic metaheuristics integration with squeeze-and-excitation resnet model.Health information science and systems · 2024Article
- Improved classification of oral cancer through a personalized transfer learning CNN architecture.Journal of oral biology and craniofacial researchArticle
Corrections and comments
- Retraction · 2025-10-07Author Unresponsive · Concerns/Issues about Referencing/Attributions · Original Data and/or Images not Provided and/or not Available · Unreliable Results and/or Conclusions ·
- Retracted
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Oral cancer can occur in different parts of the mouth, including the lips, palate, gums, and inside the cheeks. If not treated in time, it can be life-threatening. Incidentally, using CAD-based diagnosis systems can be so helpful for early detection of this disease and curing it. In this study, a new deep learning-based methodology has been proposed for optimal oral cancer diagnosis from the images. In this method, after some preprocessing steps, a new deep belief network (DBN) has been proposed as the main part of the diagnosis system. The main contribution of the proposed DBN is its combination with a developed version of a metaheuristic technique, known as the Combined Group Teaching Optimization algorithm to provide an efficient system of diagnosis. The presented method is then implemented in the "Oral Cancer (Lips and Tongue) images dataset" and a comparison is done between the results and other methods, including ANN, Bayesian, CNN, GSO-NN, and End-to-End NN to show the efficacy of the techniques. The results showed that the DBN-CGTO method achieved a precision rate of 97.71%, sensitivity rate of 92.37%, the Matthews Correlation Coefficient of 94.65%, and 94.65% F1 score, which signifies its ability as the highest efficiency among the others to accurately classify positive samples while remaining the independent correct classification of negative samples.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.