Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2023 Sep 1;24(9):2991-2995.
doi: 10.31557/APJCP.2023.24.9.2991.

Oral Cancer Prediction Using a Probability Neural Network (PNN)

Mahendrakan Kantharimuthu  1 , Malathi M  2 , Sinthia P  3 , Aloy Anuja Mary G  4 , Kins Burk Sunil N  5 , Jalal Deen K  6
Affiliations

Oral Cancer Prediction Using a Probability Neural Network (PNN)

Mahendrakan Kantharimuthu et al. Asian Pac J Cancer Prev. .

Abstract

Objective: In 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.

Methods: The 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.

Result: Due 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.

Conclusion: Oral screening is important in making a decision about oral lesions and also in avoiding delayed referrals, which reduces mortality rates.

Keywords: Discrete wavelet Transform (DWT); Malignancy; Probabilistic Neural Network (PNN); early detection; oral cancer.

PubMed Disclaimer

Conflict of interest statement

Author declares no conflict of interest.

Figures

Figure 1
Figure 1
Classification Flow Diagram
Figure 2
Figure 2
PNN Network
Figure 3
Figure 3
Depicts the Different Types of Oral Cancer Input Image, Gray-Scale Image, and Its Predicted Image Using a PNN Classifier
See this image and copyright information in PMC

References

    1. Aubreville M, Knipfer C, Oetter N, et al. Automatic Classifcation of Cancerous Tissue in Laserendomicroscopy Images of the Oral Cavity using Deep Learning. Sci Rep. 2017:7. - PMC - PubMed
    1. César Rivera. Essentials of oral cancer. Int J Clin Exp Pathol. 2015;8:11884–94. - PMC - PubMed
    1. Fua Q, Chen Y, Li Z, et al. A deep learning algorithm for detection of oral cavity squamous cell carcinoma from photographic images: A retrospective study. E Clin Med. 2020;27:1–7. - PMC - PubMed
    1. Heidari AE, Sunny Sumsum P, James Bonney L, et al. Optical Coherence Tomography as an Oral Cancer Screening Adjunct in a Low Resource Settings. IEEE J Sel Top Quantum Electron. 2019:25.
    1. Hu Z, Alsadoon A, Manoranjan P, et al. Early-Stage Oral Cavity Cancer Detection: Anisotropic Pre-Processing and Fuzzy C-Means Segmentation. 2018:8–10.