Abstract :

Oral Cancer Is One Of The Most Common And Lifethreatening Diseases Worldwide, Particularly In Developing Countries Where Tobacco Usage And Delayed Diagnosis Contribute Significantly To High Mortality Rates. Early Detection Of Oral Cancer Is Essential For Improving Patient Survival Rates And Reducing Treatment Costs. Conventional Diagnostic Techniques Mainly Rely On Visual Examination And Biopsy Procedures, Which Are Often Timeconsuming, Invasive, And Dependent On Experienced Medical Professionals. With The Advancement Of Artificial Intelligence (AI) And Deep Learning Technologies, Automated Medical Image Analysis Has Become A Promising Approach For Early Disease Detection. This Study Proposes An Oral Cancer Classification System Based On Convolutional Neural Networks (CNNs) For Automatic Detection Of Cancerous And Non-cancerous Oral Lesions From Medical Images. The Proposed System Utilizes Deep Learning Techniques To Automatically Extract Discriminative Features From Oral Cavity Images Without Manual Feature Engineering. The CNN Model Is Trained Using Labeled Oral Lesion Datasets And Optimized Through Preprocessing Techniques Such As Image Normalization, Resizing, And Data Augmentation To Enhance Classification Performance. Experimental Results Demonstrate That The Proposed Model Achieves High Accuracy, Sensitivity, And Specificity In Detecting Oral Cancer, Indicating Its Effectiveness As A Supportive Diagnostic Tool. The System Can Assist Healthcare Professionals In Early Screening, Especially In Rural And Resource-limited Environments, Thereby Improving Diagnostic Efficiency And Reducing The Burden On Medical Experts. Keywords— Oral Cancer, Deep Learning, Convolutional Neural Network (CNN), Medical Image Processing, Image Classification, Artificial Intelligence.

Published:

06-3-2026

Issue:

Vol. 26 No. 3 (2026)

Page Nos:

32-36

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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