  This paper proposes a smart handheld textural sensing medical device with
complementary Machine Learning (ML) algorithms to enable on-site Colorectal
Cancer (CRC) polyp diagnosis and pathology of excised tumors. The proposed
unique handheld edge device benefits from a unique tactile sensing module and a
dual-stage machine learning algorithms (composed of a dilated residual network
and a t-SNE engine) for polyp type and stiffness characterization. Solely
utilizing the occlusion-free, illumination-resilient textural images captured
by the proposed tactile sensor, the framework is able to sensitively and
reliably identify the type and stage of CRC polyps by classifying their texture
and stiffness, respectively. Moreover, the proposed handheld medical edge
device benefits from internet connectivity for enabling remote digital
pathology (boosting the diagnosis in operating rooms and promoting
accessibility and equity in medical diagnosis).
