Classification Of Oral Cancer From White Light Image Using A Machine Learning Algorithm For Enhanced Accuracy

Abstract

This Research Aims To Develop A New Framework For The Early Identification Of Oral Cancer Through Multimodal Data Fusion With LSTM Networks And CNNs. The Performance Of The Proposed Framework Is Tested For Sensitivity, Specificity, And Accuracy In Detecting Oral Cancer And Has A High Operational Accuracy Of 93 %. This Research Uses Two Groups Of Data Sets.One Method Involves Using Clinical Image Data With 30 Samples Processed By The CNNs For The Extraction Of Spatial Features From Possible Malignancies.The Other Strategy Has Medical Image Data Analyzed With CNN- LSTM Networks Capturing Both The Temporal Dependencies And The Contextual Information With Samples Of 45. The Proposed Framework Shows High Sensitivity And Specificity In The Detection Of Oral Cancer, Which Identifies Early-stage Lesions With The Accuracy Of 93% And Subtle Abnormalities And Shows Significance Below That Of 0.05.The Existing Concept With An CNN Was Replaced With The Proposed Concept Of Enhanced Early And Accurate Identification Of Oral Cancer, Improving Patient Outcomes And Revolutionizing Healthcare Diagnostics.

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