Strengthening Oral Cancer Detection Using WDCNN

Oral carcinoma also known as oral cavity cancer is the most prevalent cancer in the head and neck area and commonly occurs in people over the age of sixty. It commonly affects the tongue, gums, tonsils, oropharynx, and floor of the mouth. However, in recent times, it has been observed in younger peo...

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Published in2024 1st International Conference on Advances in Computing, Communication and Networking (ICAC2N) pp. 1600 - 1605
Main Authors Vemulapalli, Lavanya, Kola, Anantha Venkata Sai, Ravuri, Chaitanya Chowdary, Kanagala, Akshara
Format Conference Proceeding
LanguageEnglish
Published IEEE 16.12.2024
Subjects
Biological system modeling
Cancer
Cancer detection
DCNN
Feature extraction
Histopathological images
Image classification
Mouth
OralCancer
Pancreatic cancer
Prognostics and health management
Tongue
Wavelet analysis
Wavelet transform
Wavelet transforms
WDCNN
Online AccessGet full text
DOI10.1109/ICAC2N63387.2024.10895026

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Summary:Oral carcinoma also known as oral cavity cancer is the most prevalent cancer in the head and neck area and commonly occurs in people over the age of sixty. It commonly affects the tongue, gums, tonsils, oropharynx, and floor of the mouth. However, in recent times, it has been observed in younger people as well. Oral cancer can present as seemingly harmless issues, such as white patches or bleeding sores in the mouth, but it is not. The five-year survival rate for oral cavity cancer is 63%. According to specialists, the prognosis for those with pancreatic cancer is still poor; over 50% of patients pass away five years after being diagnosed, making early identification essential. One of the main objectives of this disease is to classify and understand oral cancer to prevent it from progressing to a more severe stage. Therefore, a proposed solution that uses Wavelet Deep Convolutional Neural Networks (WDCNN) is intended to address this issue. Digital tools for exploiting histopathological images to diagnose and classify oral cancer in its early stages. Wavelet Transform is utilized for feature extraction, while DCNN is used for image classification. By using WDCNN, this model aims to detect oral Cancer signs quickly and accurately, so doctors can treat patients sooner.
DOI:10.1109/ICAC2N63387.2024.10895026