ArticleAsian Pacific journal of cancer prevention : APJCP2023
Oral Cancer Prediction Using a Probability Neural Network (PNN).
Article in Asian Pacific journal of cancer prevention : APJCP, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed, 11 citations in OpenAlex.
- Artificial intelligence-driven clinical decision support systems for early detection and precision therapy in oral cancer: a mini review.Frontiers in oral health · 2025Review
- Enhancing Skin Cancer Classification using Efficient Net B0-B7 through Convolutional Neural Networks and Transfer Learning with Patient-Specific Data.Asian Pacific journal of cancer prevention : APJCP · 2024Article
- Oral squamous cell carcinoma detection using EfficientNet on histopathological images.Frontiers in medicine · 2023Article
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Authors and funding
6 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveIn India, usually, oral cancer is mostly identified at a progressive stage of malignancy. Hence, we are motivated to identify oral cancer in its early stages, which helps to increase the lifetime of the patient, but this early detection is also more challenging.
methodsThe proposed research work uses a probabilistic neural network (PNN) for the prediction of oral malignancy. The recommended work uses PNN along with the discrete wavelet transform to predict the cancer cells accurately. The classification accuracy of the PNN model is 80%, and hence this technique is best for the prediction of oral cancer.
resultDue to heterogeneity in the appearance of oral lesions, it is difficult to identify the cancer region. This research work explores the different computer vision techniques that help in the prediction of oral cancer.
conclusionOral screening is important in making a decision about oral lesions and also in avoiding delayed referrals, which reduces mortality rates.
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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.