• Aurchana.P
• Dhanalakshmi.P

The aim of the study is to discover the histopathological variant of Oral Squamous Cell Carcinoma. The variants incorporate verrucous carcinoma, adenoid carcinoma, spindle cell squamous cell carcinoma, basaloid squamous cell carcinoma and papillary squamous cell carcinoma. Every variation has a unique histomorphological appearance. Each pathologist while evaluating this lesion leads to mistakes. To overcome this, Computer Aided diagnosis gives the exact detection of these variants. A novel approach of Convolutional neural network has been utilized to recognize the highlights and group the variations of oral squamous cell carcinoma. The proposed convolution design forms the various layers containing the diverse pooling parameters. The prediction of disease classification gives the satisfactory results.

Oral Squamous Cell Carcinoma (OSCC),Verrucous Carcinoma (VC),Adenoid squamous cell Carcinoma(ADSCC), Spindle cell/sarcomatoid carcinoma(SCSC) ,adenosquamous carcinoma(ASC) ,Basaloid Squamous Cell Carcinoma(BSCC), papillary squamous cell carcinoma (PSCC)

"Histopathological variants of Oral squamous cell carcinoma detection using Convolutional Neural Network", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 1, page no.221-225, January-2019, Available :http://www.jetir.org/papers/JETIR1901931.pdf

"Histopathological variants of Oral squamous cell carcinoma detection using Convolutional Neural Network", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 1, page no. pp221-225, January-2019, Available at : http://www.jetir.org/papers/JETIR1901931.pdf