An Artificial Tactile Neuron Enabling Spiking Representation of Stiffness and Disease Diagnosis

An Artificial Tactile Neuron Enabling Spiking Representation of Stiffness and Disease Diagnosis

© 2022 The Authors. Advanced Materials published by Wiley-VCH GmbH.

Détails bibliographiques
Publié dans:Advanced materials (Deerfield Beach, Fla.). - 1998. - 34(2022), 24 vom: 18. Juni, Seite e2201608
Auteur principal: Lee, Junseok (Auteur)
Autres auteurs: Kim, Seonjeong, Park, Seongjin, Lee, Jaesang, Hwang, Wonseop, Cho, Seong Won, Lee, Kyuho, Kim, Sun Mi, Seong, Tae-Yeon, Park, Cheolmin, Lee, Suyoun, Yi, Hyunjung
Format: Article en ligne
Langue:English
Publié: 2022
Accès à la collection:Advanced materials (Deerfield Beach, Fla.)
Sujets:Journal Article artificial tactile neurons disease diagnosis elastography neuromorphic sensors ovonic threshold switching piezoresistive sensors spiking neural networks
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520 |a Mechanical properties of biological systems provide useful information about the biochemical status of cells and tissues. Here, an artificial tactile neuron enabling spiking representation of stiffness and spiking neural network (SNN)-based learning for disease diagnosis is reported. An artificial spiking tactile neuron based on an ovonic threshold switch serving as an artificial soma and a piezoresistive sensor as an artificial mechanoreceptor is developed and shown to encode the elastic stiffness of pressed materials into spike frequency evolution patterns. SNN-based learning of ultrasound elastography images abstracted by spike frequency evolution rate enables the classification of malignancy status of breast tumors with a recognition accuracy up to 95.8%. The stiffness-encoding artificial tactile neuron and learning of spiking-represented stiffness patterns hold a great promise for the identification and classification of tumors for disease diagnosis and robot-assisted surgery with low power consumption, low latency, and yet high accuracy 
650 4 |a Journal Article 
650 4 |a artificial tactile neurons 
650 4 |a disease diagnosis 
650 4 |a elastography 
650 4 |a neuromorphic sensors 
650 4 |a ovonic threshold switching 
650 4 |a piezoresistive sensors 
650 4 |a spiking neural networks 
700 1 |a Kim, Seonjeong  |e verfasserin  |4 aut 
700 1 |a Park, Seongjin  |e verfasserin  |4 aut 
700 1 |a Lee, Jaesang  |e verfasserin  |4 aut 
700 1 |a Hwang, Wonseop  |e verfasserin  |4 aut 
700 1 |a Cho, Seong Won  |e verfasserin  |4 aut 
700 1 |a Lee, Kyuho  |e verfasserin  |4 aut 
700 1 |a Kim, Sun Mi  |e verfasserin  |4 aut 
700 1 |a Seong, Tae-Yeon  |e verfasserin  |4 aut 
700 1 |a Park, Cheolmin  |e verfasserin  |4 aut 
700 1 |a Lee, Suyoun  |e verfasserin  |4 aut 
700 1 |a Yi, Hyunjung  |e verfasserin  |4 aut 
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773 1 8 |g volume:34  |g year:2022  |g number:24  |g day:18  |g month:06  |g pages:e2201608 
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