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Automatic disease diagnosis using optimised weightless neural networks for low-power wearable devices

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Automatic disease diagnosis using optimised weightless neural networks for low-power wearable devices

Content Provider	IET Digital Library
Author	Cheruku, Ramalingaswamy Edla, Damodar Reddy Kuppili, Venkatanareshbabu Dharavath, Ramesh Beechu, Nareshkumar Reddy
Abstract	Low-power wearable devices for disease diagnosis are used at anytime and anywhere. These are non-invasive and pain-free for the better quality of life. However, these devices are resource constrained in terms of memory and processing capability. Memory constraint allows these devices to store a limited number of patterns and processing constraint provides delayed response. It is a challenging task to design a robust classification system under above constraints with high accuracy. In this Letter, to resolve this problem, a novel architecture for weightless neural networks (WNNs) has been proposed. It uses variable sized random access memories to optimise the memory usage and a modified binary TRIE data structure for reducing the test time. In addition, a bio-inspired-based genetic algorithm has been employed to improve the accuracy. The proposed architecture is experimented on various disease datasets using its software and hardware realisations. The experimental results prove that the proposed architecture achieves better performance in terms of accuracy, memory saving and test time as compared to standard WNNs. It also outperforms in terms of accuracy as compared to conventional neural network-based classifiers. The proposed architecture is a powerful part of most of the low-power wearable devices for the solution of memory, accuracy and time issues.
Starting Page	122
Ending Page	128
Page Count	7
Volume Number	4
e-ISSN	20533713
Issue Number	Issue 4, Aug (2017)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/htl/4/4
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/htl.2017.0003
Journal	Healthcare Technology Letters
Publisher	The Institution of Engineering and Technology
Publisher Date	2017-05-19
Access Restriction	Open
Rights License	Creative Commons Attribution -Non Commercial License (http://creativecommons.org/licenses/by-nc/3.0/)
Subject Keyword	Automatic Disease Diagnosis Bioinspired Based Genetic Algorithm Biology And Medical Computing Biology Computing Biomedical Measurement And Imaging Diseases Genetic Algorithm Low Power Wearable Device Memory Circuit Memory Constraint Modified Binary TRIE Data Structure Neural Computing Technique Neural Nets Noninvasive Device Optimisation Technique Optimised Weightless Neural Network Pain Free Device Patient Diagnosis Patient Diagnostic Method And Instrumentation Quality of Life Random-access Storage Variable Sized Random Access Memories
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
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