Automatic disease diagnosis using optimised weightless neural networks for low‐power wearable devices. Issue 4 (19th May 2017)

• Record Type: Journal Article
• Authors: Cheruku, Ramalingaswamy; Edla, Damodar Reddy; Kuppili, Venkatanareshbabu; Dharavath, Ramesh; Beechu, Nareshkumar Reddy
• Is Part Of: Healthcare technology letters. Volume 4:Issue 4(2017)
• Page Start: 122
• Page End: 128
• Publication Date: 2017-05-19
• Subjects: diseases -- patient diagnosis -- neural nets -- random‐access storage -- genetic algorithms -- biology computing; automatic disease diagnosis -- optimised weightless neural networks -- low power wearable devices -- noninvasive devices -- pain free devices -- quality of life -- memory constraint -- variable sized random access memories -- modified binary TRIE data structure -- bioinspired based genetic algorithm; Biomedical engineering -- Periodicals; Medical technology -- Periodicals; 610.28
• DOI: 10.1049/htl.2017.0003
• Languages: English
• ISSNs: 2053-3713

Abstract

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.