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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:10.55041/IJSREM18667

Oral Cancer Detection in its Nascent Stages Using 3D CNN: A Survey

Stream/Area:

-

Subject:

-

Paper ID:

10.55041/IJSREM18667

Volume:

7 |

4 |

Apr 2023

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
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Not assigned

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

PS

Prof. Sagari

SS

Saaransh Solanki

PG

Pranav Goel

SJ

Samyak Jain

MS

Mehek Shaik

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Abstract

oral cavity cancer also called squamous cell carcinoma is the sixth most frequent cancer overall, it is a rising public health concern that is spreading across the globe. If not detected in earlier stages, it might be severe generally, and underlying lesions induce it. Therefore, it might be difficult to locate the lesion area and if not cured in its nascent stages, it might reach stage IV cancer, called the metastatic stage. To come up with a solution, we tend to use the three-dimensional CNN processes malignancies with image data and 3DCNN structures to classify benign and aggressive oral tumors. By identifying the kind of tumor region susing various kinds of knowledge here on periodic alterations in tumor enhancers, CNN was utilized in Test with visual categorization improvement rates. Analyzed, categorized, and future study areas are identified. The datasets utilized in the review articles are covered in detail, as are the pre- processing methods, the deep learning frameworks employed, etc. It presents a comparison of the same.

KEYWORDS

Oral Cancer Oral lesions Oral Cavity 3D CNN Pre-processing tumors malignant deep-learning

Cite This Article

Prof. Sagari, Saaransh Solanki, Pranav Goel, Samyak Jain, Mehek Shaik, "Oral Cancer Detection in its Nascent Stages Using 3D CNN: A Survey," International Journal of Scientific Research in Engineering and Management, vol. 7, no. 4, Apr 2023. https://doi.org/10.55041/IJSREM18667

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Publication Date

6/3/2023

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Double-Blind Peer Review

Review Rounds: 2

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The authors declare no conflict of interest related to this research work.

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