Abstract:
Oral squamous cell carcinoma (OSCC), is a type of cancer that causes the loss of the structural formation of layers and
membranes in the oral cavity region. With the recent advent of Deep learning (DL) in biomedical image classification, the automated
early diagnosis of oral histopathological images can aid in effective treatment of oral cancer. This work attempts to perform an automated
classification of benign and malignant oral biopsy histopathological images by implementing a DL-based convolutional neural network
(CNN) model for the initial analysis of OSCC. For this research, four recently developed candidate pre-trained DL-CNN models namely
NASNetLarge, InceptionNet, Xception, and DenseNet201 are selected through the approach of transfer learning. These pre-trained models
are then modified with additional layers for effective OSCC detection. The efficacy of these modified models is examined on an oral
cancer histopathological image database. It is examined that the pre-trained DenseNet201 model with modified structure has surpassed
other models in terms of performance parameters by recording an accuracy of 91.25% and is considered as our proposed DL-CNN model.
