Graph Convolutional Neural Networks for Alzheimer's Classification with Transfer Learning and HPC Methods

Kumar, A and Balaji, V and Chandrashekar, MA and Dukkipati, A and Vadhiyar, S (2022) Graph Convolutional Neural Networks for Alzheimer's Classification with Transfer Learning and HPC Methods. In: 36th IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2022, 30 May 2022 through 3 June 2022, pp. 186-195.

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Abstract

GCNs have been increasingly used for the classification of brain functional networks to aid early prediction of neurodegenerative diseases. It is important to analyze the performance and capabilities of these GCNs for the classification and obtain insights on how and when the GCNs can be used. In this work, we perform detailed analyses of the performance of GCNs for the classification of brain functional networks of Alzheimer's data. We study the impact of the various parameters including the thresholds used for graph generation, graph sizes, data sizes or the number of subjects, and classification accuracy across different visits of the subjects. We have also developed a transfer learning approach to train using one dataset and apply the weights on another dataset to make use of larger data sizes. Finally, we have developed GPU-based acceleration methods to decrease the training time. We find that the accuracy of the models improves when taking into account all visits of the subjects and also improves with increasing graph sizes. The use of transfer learning has also improved classification accuracy. Finally, the use of CUDA streams for asynchronous computations has resulted in a reduction in execution times by up to 60. © 2022 IEEE.

Item Type:	Conference Paper
Publication:	Proceedings - 2022 IEEE 36th International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2022
Publisher:	Institute of Electrical and Electronics Engineers Inc.
Additional Information:	The copyright for this article belongs to the IEEE.
Keywords:	Graph neural networks; Graphics processing unit; Learning systems; Neurodegenerative diseases; Transfer learning, Alzheimer; Alzheimer data; Brain functional networks; Classification accuracy; Data size; GCN; GPU accelerations; Graph sizes; Performance; Transfer learning, Convolutional neural networks
Department/Centre:	Division of Electrical Sciences > Computer Science & Automation
Date Deposited:	06 Sep 2022 07:11
Last Modified:	06 Sep 2022 07:11
URI:	https://eprints.iisc.ac.in/id/eprint/76485

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