The classification of oral squamous cell carcinoma (OSCC) by means of transfer learning

Patients that are diagnosed with oral cancer has more than 83% sur- vival chance if it is detected in its early stages. However, through conventional labour-intensive means, only 29% cases are detected. It is worth to mention that 90 % of oral cancer is the Oral Squamous Cell Carcinoma (OSCC) and is often caused by smoking and alcohol consumption. Computer aided diagnostics could further increase the rate of detection of this form of oral cancer. The present study sought to employ a class of deep learning technique known as transfer learning. The Inception V3 pre-trained convolutional neural network model is used to ex- tract the features from texture-based images. Consequently, the malignant and benign nature of the cancel is identified from three different machine learning models, i.e., Support Vector Machine (SVM), k-Nearest Neighbors (kNN) and Random Forest (RF). It was shown from the study that an average of 91% classification accuracy was obtained from the test and validation dataset from the Inception V3-RF pipeline. The outcome of the present study could serve useful in an objective-based automatic diagnostic of OSCC and hence could possibly increase its detection