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.