Oral cancer is the most common cancer found in both men and women. Early Detection of Oral Cancer is important in saving life. Dental Radiographs assists experts in identifying cancers grown inside the mouth. To help radiologists, Computer Aided Detection and Computer Aided Diagnosis algorithms are developed. The algorithms help to identify cancers by reducing the need for Biopsy. This paper gives a review of Computer Aided Techniques that have been developed for detection and classification of oral cancers.

Radiographs, Computer Aided Detection, Computer Aided Diagnosis

[1] Arlene Guagliano, �Oral Cancer: Early Detection Saves Life�, Dental Tribune, Middle East and African Edition, Page No (6-7), June � August 2011.
[2] National Cancer Institute Website, www.cancer.gov
[3] A.S.Jadhav, S.Banerjee, P.K.Dutta, R.R. Paul, M.Pal, P. Banerjee, K. Chaudhuri and J. Chatterjee, �Quantitative analysis of histopathological features of precancerous lesion and condition using Image Processing Techniques�, 19th IEEE Symposium on Computer-Based Medical Systems, ISBN 0-7695-2517-1, Page No (231 � 236), June 22 � 23, 2006.
[4] Ghassan Hamarneh, Artur Chodorowski and Tomas Gustavsson, �Active Contour models: Application to oral Lesion detection in color images�, IEEE Conference in Systems, Man and Cybernetics, Nashville, TN, USA, Page No (2458 � 2463), October 2000.
[5] Yung nien Sun, Yi-ying Wang, Shao-chien Chang, Li-wha Wu and Sen � tien Tsai, �Color � based tumor segmentation for the automated estimation of oral cancer parameters�, Microscopy Research and Technique, Volume - 73, Issue - 1, Page No (5- 13), June 2009.
[6] Man Kin Derek Ho, �Watershed segmentation algorithm for medical confocal image analyses towards in vivo early cancer detection�, National Nanotechnology Infrastructure Network. Page No 14 � 16.
[7] Woonggyu Jung, Jun Zhang, Jungrae Chung, Petra Wilder � Smith, Matt Brenner, J. Stuart Nelson and Zhongping Chen, �Advances in Oral Cancer Detection using Optical Coherence Tomography�, IEEE Journal of Selected Topics in Quantum Electronics, Volume - 11, No.4, Page No (811 � 817), 2005.
[8] R.Hari Kumar, C.Ganesh babu, P.Shri vignesh, �Earlier Detection of Oral cancer from fuzzy based photo plethysmography�, International Journal of Soft Computing and Engineering, Volume � 2, No � 1, Page No (128 � 133), March 2012.
[9] A.Banumathi, J.Praylin Mallika , S.Raju and V.Abhai Kumar, �Automated Diagnosis and Severity Measurement of Cyst in Dental X-ray Images using Neural Network�, International Journal of Biomedical Soft Computing and Human Sciences, Volume - 14, Issue - 2, Page No (103 � 108), 2009.
[10] A.Banmathi, A.Kannammal, R.Arthee, S.Raju and V.Abhai Kumar.V, �Automated Diagnosis and Severity Measurement of Cysts in Dental X-ray Images using Neural Network�, International Journal of Biomedical Soft Computing and Human Sciences, Volume -11, Issue - 1, Page No (15 � 19), 2005.
[11] C.Venugopal, S.Nazeer Shaiju, Balan Anita and R.S.Jayasree, �Autofluorescence Spectroscopy Augmented by Multivariate Analysis as a Potential Noninvasive Tool for Early Diagnosis of Oral Cavity Disorders�, Journal of Photomedicine and Laser Surgery, Volume - 31, (12), Page No (605-612), 2013.
[12] Ranjan Rashmi Paul, Anirban Mukherjee, Pranab K. Dutta, Swapna Banerjee, Mousumi, Pal, Jyotirmoy Chatterjee and Keya Chaudhuri, �A novel wavelet neural network based pathological stage detection technique for an oral precancerous condition�, Journal of Clinical Pathology, Volume - 58 (9), Page No (932 �938), 2005.
[13] Simon Kent, �Diagnosis of oral cancer using Genetic Programming � A Technical Report�, CSTR-96-14 CNES-96-04, 1996.
[14] S.Prasanna, K.Govinda and U.Senthil Kumaran, �An evaluation study for oral cancer detection using Data Mining Classification Techniques�, International Journal of Advanced Research in Computer Science, Volume � 3, Issue - 1, Page No (142 � 146), 2012.
[15] Neha Sharma and Hari Om, �Framework for early detection and prevention of oral cancer using Data Mining�, International Journal of Advances in Engineering and Technology, Volume � 4, Issue - 2, Page No (302 � 310), 2012.
[16] P.Konstantinos, Exarchos, Yorgos Goletsis and Dimitrios I. Fotiadis, �Unification of heterogeneous data towards the prediction of oral cancer reoccurrence�, AIAI-2009 Workshops Proceedings, Page No (24 � 35), 2009.
[17] M.M. Krishnan, U.R.Acharya, C. Chakraborty and A.K. Ray, �Automated Diagnosis of Oral Cancer Using Higher Order Spectra Features and Local Binary Pattern: A Comparative Study�, Technology in Cancer Research and Treatment, 10 (5), Page No (443-455), 2011.
[18] Ingrid Nurtanio, Renwi Astuti, I Ketut Eddy Purnama, Mohamad Hariadi and Mauridhi Hery Purnomo, �Classifying Cyst and Tumor Lesion using Support Vector Machine Based on Dental Panoramic Images Texture Features�, IAENG International Journal of Computer Science, 40(1), 2013.
[19] Ingrid Nurtanio, I Ketut Eddy Purnama, Mohamad Hariadi, and Mauridhi Hery Purnomo, �Cyst and Tumor Lesion Segmentation on Dental Panoramic Images using Active Contour Models�, IPTEK The Journal of Technology and Science, Volume � 22, No - 3, Page No (152 � 158), 2011.
[20] Lalit Gupta, Sarif Kumar Naik and Srinivasan Balakrishnan, �A new feature selection and classification scheme for screening of oral cancer using laser induced fluorescence�, Proceedings of the First International Conference on Biometrics (ICMB�08 ), ISBN:3-540-77410-6 978-3-540-77410-5, Page No (1-8), Jan 4 � 5, 2008.
[21] Sebastian Steger, Marius Erdt, Gianfranco Chiari and Georgios Sakas, �Feature Extraction from Medical Images for an oral cancer reoccurrence prediction environment�, World Congress on Medical Physics and Biomedical Engineering, Munich, Germany, Page No (97 � 100), Sep 7 -12, 2009.
[22] G. Landini, �Quantitative analysis of the epithelial lining architecture in radicular cysts and odontogenic keratocysts�, Head & Face Medicine, Volume � 2, Feb 2006.
[23] Yusaku Nishi, Keiichi Horio, Kentaro Saito, Manabu Habu and Kazuhiro Tominaga, �Discrimination of Oral Mucosal Disease Inspired by Diagnostic Process of Specialist�, Journal of Medical and Bioengineering, Volume - 2, No - 1, Page No (57 � 61), 2013.
[24] Yung �nien Sun, Yi-ying Wang, Shao-chien Chang, Li-wha Wu and Sen � tien Tsai, �A color � based approach for automated segmentation in Tumor Tissue Classification�, Proceedings of the 29th Annual International conference of the IEEE Engineering in Medicine and Biology Society, 2007.
[25] Yung nien Sun, Yi-ying Wang, Shao-chien Chang, Li-wha Wu and Sen � tien Tsai, �Color � based tumor segmentation for the automated estimation of oral cancer parameters�, Microscopy Research and Technique, 73(1), Page No (5- 13), 2010.
[26] Ji Wan Han. A Toby Breckon, David Randell, Gabriel Landini, �Radicular cysts and odontogenic keratocysts epithelia classification using Cascaded Haar classifiers�, Proceedings of the 12th Annual Conference on Medical Image Understanding and Analysis, 2008.
[27] Tathagata Ray, D. Shivashanker Reddy, Anirban Mukherjee, Jyotirmoy Chatterjee, Ranjan R. Paul and Pranab K. Dutta, �Detection of constituent layers of histological oral sub-mucous fibrosis: Images using the hybrid segmentation algorithm� Science Direct, Oral Oncology, Volume - 44, Issue � 12, Page No (1167 � 1171), Dec 2008.
[28] S.Venkatakrishnan, V. Ramalingam and S.Palanivel, �Classification of Oral Sub mucous Fibrosis using SVM�, International Journal of Computer Applications, Volume - 78, No - 3, Page No (8-11), 2013.
[29] Muthu Rama Krishnan.M, Chandran Chakraborthy and Ajoy Kumar Ray, �Wavelet based texture classification of oral histopathological sections�, International Journal of Microscopy, Science, Technology, Applications and Education, Volume � 2, Page No (897-906), 2011.
[30] M.R.Brickley, J. G. Cowpe and J. P. Shepherd, �Performance of a computer simulated neural network trained to categorise normal, premalignant and malignant oral smears�, Journal of Oral Pathology and Medicine, Volume - 25, Issue - 8, Page No (424 � 428), 1996
[31] A. Chodorowski, U. Mattsson and T. Gustavsson, �Oral Lesion classification using true color images�, Proceedings of SPIE (International Society for Optics and Photonics), Volume � 3661, Page No (1127 � 1138), 1999.
[32] Neha Sharma, Nigdi Pradhikaran and Akurdi, �Comparing the performance of data mining techniques for oral cancer prediction�, International Conference on Communication, Computing & Security (ICCCS�11), ISBN: 978-1-4503-0464-1, Page No (433 � 438), New York, USA, 2011.
[33] R. Jaya Suji and S.P.Rajagopalan, �An automatic oral cancer classification using Data Mining Techniques�, International Journal of Advanced Research in Computer and Communication Engineering, Volume � 2, Issue - 10, Page No 3(759 � 3765).