UGC Approved Journal no 63975(19)

ISSN: 2349-5162 | ESTD Year : 2014
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Published in:

Volume 10 Issue 8
August-2023
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

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Published Paper ID:
JETIR2308518


Registration ID:
523686

Page Number

f173-f176

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Title

AN EXPERIMENTAL STUDY ON THE MACHINE LEARNING TECHNIQUES FOR ORAL CANCER CLASSIFICATION

Abstract

Oral Cancer is a considered as complex and wide spread cancer as it is rapidly evolving with a low median survival rate. Currently, duration and cost of the treatment process is long and very high due to its high recurrence and mortality rates. Accurate early diagnosis and prognosis prediction of cancer are become more essential to enhance the patient’s survival rate using medical oncology. Using advanced technologies and machine learning techniques early detection of the deadly disease are made possible. Enabling automated detection and classification of the malignant lesions along computing the prognosis of the disease can be carried out using machine learning techniques with low cost and early diagnosis of the disease. In this paper, experimental study on machine learning technique for oral cancer classification has been carried on basis of defining the disease, diagnosis of the disease, classification of the disease in terms of stages of the lesion on basis of structure and finally prognosis and survival rate. Machine learning model is capable of learning the complex lesion patterns of the disease extracted from feature extraction and feature selection model. Classification of the patterns has been represented into stages. Classification results are highly discriminant with enhanced classification rate on the dynamic characteristics of the images. Evaluation of the technique is estimated using various datasets. The evaluation of the classification technique is based on the feature extraction and feature selection methods. Finally the performance analysis has done with respect to classification accuracy and execution time and attains the effective results on the cross fold validation of the dataset using confusion matrix on basis of precision, recall and f measure.

Key Words

Machine Learning, Oral Cancer, Classification, Staging, Feature Selection, Feature Extraction

Cite This Article

"AN EXPERIMENTAL STUDY ON THE MACHINE LEARNING TECHNIQUES FOR ORAL CANCER CLASSIFICATION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 8, page no.f173-f176, August-2023, Available :http://www.jetir.org/papers/JETIR2308518.pdf

ISSN


2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"AN EXPERIMENTAL STUDY ON THE MACHINE LEARNING TECHNIQUES FOR ORAL CANCER CLASSIFICATION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 8, page no. ppf173-f176, August-2023, Available at : http://www.jetir.org/papers/JETIR2308518.pdf

Publication Details

Published Paper ID: JETIR2308518
Registration ID: 523686
Published In: Volume 10 | Issue 8 | Year August-2023
DOI (Digital Object Identifier): http://doi.one/10.1729/Journal.35841
Page No: f173-f176
Country: Coimbatore, Tamilnadu, India .
Area: Science & Technology
ISSN Number: 2349-5162
Publisher: IJ Publication


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