A Smart Handheld Edge Device for On-Site Diagnosis and Classification of Texture and Stiffness of Excised Colorectal Cancer Polyps

TL;DR

This paper introduces a smart handheld device equipped with tactile sensing and machine learning for on-site diagnosis and classification of colorectal cancer polyps, enhancing accuracy and accessibility during surgeries.

Contribution

A novel handheld edge device combining tactile sensing and dual-stage ML algorithms for real-time CRC polyp diagnosis and classification.

Findings

Accurately classifies polyp type and stiffness using tactile images.

Operates reliably in occlusion-free, illumination-resilient conditions.

Enables remote digital pathology via internet connectivity.

Abstract

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).