A Review of Different Methods for Automatic Diagnosis of Oral Cancer
Anuradha S. Pandit1, V.V. Dixit2

1Ms. Anuradha S. Pandit, Assistant Professor, Department of E&TC Engineering, Shrimati Kashibai Navale College of Engineering, Pune, India.
2Dr. Vaibhav Vitthalrao Dixit, Principal and Director, Department of Engineering, RMD Sinhgad Technical Institutes Campus, Warje, Pune, India.

Manuscript received on May 25, 2020. | Revised Manuscript received on June 29, 2020. | Manuscript published on July 30, 2020. | PP: 1143-1145 | Volume-9 Issue-2, July 2020. | Retrieval Number: B4177079220/2020©BEIESP | DOI: 10.35940/ijrte.B4177.079220
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Oral cancer is having 6th rank out of all cancers in the world. There might be tumor in salivary glands, tonsils and also in neck, head, face and oral cavity. Oral cancer can be diagnosed with methods like biopsy or with screening method. In biopsy method small sample of tissue is being removed from affected part of the body and tested under microscope. But biopsy is invasive and painful. Also pathological analysis of it is time consuming. Screening method is non invasive. Early detection is possible with screening method which is necessary for improvement of survival rate. This paper presents different screening methods for detection of oral cancer. Optical Coherence Tomography and a variety of Machine Learning based techniques, including Artificial Neural Networks (ANNs), Bayesian Networks (BNs), Tree Boost Model are discussed in this paper.
Keywords: OSCC, GLCM, SVM, Optical Coherence Tomography, Neo Maker, Deep Convolution Network.