The Segmentation of Oral Cancer MRI Images using Residual Network

The segmentation of tumour from a cancer MRI images in image processing is classic research area of interest and a tedious task. Manually segmenting the MRI images is very time consuming and liable to errors. Many researchers have done investigation using deep neural network in segmenting the oral MRI images as they poses higher performance in segmenting the oral cancer images automatically. Owing to their gradient dissemination and complexity issues, the CNN takes more time and excess computational power in training the images. Our aim is build an automated technique for the segmentation of oral cancer images using Residual learning networks (ResNet) to render the complications of gradient dissemination caused by CNN. ResNet attains higher accuracy and trains the images faster compared to CNN. To accomplish this, ResNet counts a skip connection parallel to convolution neural network layers. The verification accuracy of the proposed technique has been carried out on oral cancer (lip and tongue) images dataset. The results of proposed technique shows a better accuracy, dice co-efficient, specificity and precision of 0.92, 0.95, 0.94, 0.96 respectively and computational time of 63 mins.