Authors:
Pavan kumar Panakanti,Sammulal Porika,SK Yadav,DOI NO:
http://doi.org/10.26782/jmcms.2019.10.00034Keywords:
Diabetes detection,Convolutional Neural Networks,CNN,Capsule Networks,CapsNet,Abstract
Diabetes detection has become an important task for medical practitioners in India and abroad. Researchers and scientists have been working on this problem actively. Machine learning has been contributing majorly to systems, techniques and solutions for diabetes detection problem. Yet there are challenges which remain to be addressed. Recently convolution based machine learning techniques have evolved to give efficient results in various domains. They have shown applicability over range of problems. So here recent architectures of Convolution based machine learning models like Convolutional Neural Networks (CNN) and Capsule Networks (CapsNet) are discussed. Also, application of these recent models is presented here. Additionally, challenges faced by current Diabetes detection systems are discussed. Along with these challenges CapsNet architecture for text analytics is presented. This CapsNet architecture is closest to Diabetes detection problem in terms of structure and arrangement of data to be handled. Thus in future this architecture and its variants can be applied for Diabetes detection.Refference:
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