An Examen of Oral Carcinoma using Machine Learning Approaches

Abstract

Oral squamous cell carcinoma is the 8th most fast-spreading cancer, globally. Detection and treatment of Oral cancer are the most important aspects that are needed nowadays in this fast-moving world. Nanotechnology and machine learning are two predominant and upcoming technologies for detecting and classifying cancer. Machine learning algorithms are widely used in the early-stage detection and classification of cancer. These detections can be even performed using smart devices like cell phone cameras. Nanotechnology sets a new trend and makes a new revolution in the world of information with the help of nanoparticles like gold nanoparticles, nano-virus, nanohydroxyapatite, and so on. Nanoparticles are, being smaller in size, effectively used to stop the further spread of cancer to other body parts. Accurate detection of Oral squamous cell carcinoma is a very important phase of OSCC treatment. Feature extraction of OSCC classification which is attained using various machine learning algorithms such as SVM, Naïve Bayes, and CNN. Deep learning has attained outstanding performance in early-stage cancer detection with a large dataset and the results are obtained with high accuracy in OSCC early-stage detection. It seeks to diagnose oral cancer with more precision and in less time. Future oral cancer deaths might be reduced by performing early detection. Of these algorithms, CNN has been improved in every research and attained an accuracy of ran up to 96.6%.