An Ensemble Deep Neural Network Approach for Oral Cancer Screening

Abstract

One of the ways to reduce oral cancer mortality rate is diagnosing oral lesions at initial stages to classify them as precancerous or normal lesions. During routine oral examination, oral lesions are normally screened manually. In a low resource setting area where there is lack of medical facilities and also medical expertise, an automated mechanism for oral cancer screening is required. The present work is an attempt towards developing an automated system for diagnosing oral lesions using deep learning techniques. An ensemble deep learning model that combines the benefits of Resnet-50 and VGG-16 has been developed. This model has been trained with an augmented dataset of oral lesion images. The model outperforms other popularly used deep learning models in performing the classification of oral images. An accuracy of 96.2%, 98.14% sensitivity and 94.23% specificity was achieved with the ensemble deep learning model.

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