  In this study, with the goal of reducing the early detection miss rate of
colorectal cancer (CRC) polyps, we propose utilizing a novel hyper-sensitive
vision-based tactile sensor called HySenSe and a complementary and novel
machine learning (ML) architecture that explores the potentials of utilizing
dilated convolutions, the beneficial features of the ResNet architecture, and
the transfer learning concept applied on a small dataset with the scale of
hundreds of images. The proposed tactile sensor provides high-resolution 3D
textural images of CRC polyps that will be used for their accurate
classification via the proposed dilated residual network. To collect realistic
surface patterns of CRC polyps for training the ML models and evaluating their
performance, we first designed and additively manufactured 160 unique realistic
polyp phantoms consisting of 4 different hardness. Next, the proposed
architecture was compared with the state-of-the-art ML models (e.g., AlexNet
and DenseNet) and proved to be superior in terms of performance and complexity.
