Intelligent explainable optical sensing on Internet of nanorobots for disease detection

Abstract

Combining deep learning (DL) with nanotechnology holds promise for transforming key facets of nanoscience and technology. This synergy could pave the way for groundbreaking advancements in the creation of novel materials, devices, and applications, unlocking unparalleled capabilities. In addition, monitoring psychological, emotional, and physical states is challenging, yet recent advancements in the Internet of Nano Things (IoNT), nano robot technology, and DL show promise in collecting and processing such data within home environments. Using DL techniques at the edge enables the processing of Internet of Things device data locally, preserving privacy and low latency. We present an edge IoNT system that integrates nanorobots and DL to identify diseases, generating actionable reports for medical decision-making. Explainable artificial intelligence enhances model transparency, aiding clinicians in understanding predictions. Intensive experiments have been carried out on Kvasir dataset to validate the applicability of the designed framework, where the accuracy of results demonstrated its potential for in-home healthcare management.