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Generative Adversarial Networks for Lupus Diagnostics

Authors:

Pradeep Periasamy,

Vetria L. ByrdAuthors Info & Claims

PEARC '19: Practice and Experience in Advanced Research Computing 2019: Rise of the Machines (learning)

Article No.: 104, Pages 1 - 8

https://doi.org/10.1145/3332186.3338102

Published: 28 July 2019 Publication History

Abstract

The recent boom of Machine Learning frameworks like Generative Adversarial Networks (GAN), Deep Convolution Generative Adversarial Networks (DCGAN) and the development of high-performance computing for big data analysis has the potential to be highly beneficial in many domains and fittingly in the early detection of chronic diseases. The clinical heterogeneity of one such chronic autoimmune disease like Systemic Lupus Erythematosus (SLE), commonly referred to as Lupus, makes it difficult for medical diagnostics. This research employs unsupervised deep learning mechanisms to identify clinical manifestations of lupus from publicly available anonymous pictures of persons who present with cutaneous lesions like the butterfly rash, commonly seen in patients diagnosed with Lupus. We demonstrate the use of artificially generated butterfly rash images generated from GAN to train the discriminator model that differentiates Lupus from its other counter skin diseases using a Neural Network Classifier, as a use-case example. The expected outcomes are to help reduce the time in detection and treatment by gathering insights from its huge heterogeneous data clusters.

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Cited By

Falaschetti LCampanella SCurina SMartini GPierleoni PBelli ATurchetti CPalma L(2024)An Embedded Vision System for Autoimmune Skin Diseases Classification Based on Deep Learning: A Preliminary Study2024 IEEE Sensors Applications Symposium (SAS)10.1109/SAS60918.2024.10636541(1-6)Online publication date: 23-Jul-2024
https://doi.org/10.1109/SAS60918.2024.10636541
Dey SMansoor N(2023)A Butterfly Malar Rash Detection Model for Early Systemic Lupus Erythematosus Diagnosis2023 26th International Conference on Computer and Information Technology (ICCIT)10.1109/ICCIT60459.2023.10441593(1-6)Online publication date: 13-Dec-2023
https://doi.org/10.1109/ICCIT60459.2023.10441593

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cover image ACM Other conferences

PEARC '19: Practice and Experience in Advanced Research Computing 2019: Rise of the Machines (learning)

July 2019

775 pages

ISBN:9781450372275

DOI:10.1145/3332186

General Chair:
Tom Furlani
Roswell Park Comprehensive Cancer Center

Copyright © 2019 ACM.

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Published: 28 July 2019

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PEARC '19

PEARC '19: Practice and Experience in Advanced Research Computing

July 28 - August 1, 2019

IL, Chicago, USA

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Cited By

Falaschetti LCampanella SCurina SMartini GPierleoni PBelli ATurchetti CPalma L(2024)An Embedded Vision System for Autoimmune Skin Diseases Classification Based on Deep Learning: A Preliminary Study2024 IEEE Sensors Applications Symposium (SAS)10.1109/SAS60918.2024.10636541(1-6)Online publication date: 23-Jul-2024
https://doi.org/10.1109/SAS60918.2024.10636541
Dey SMansoor N(2023)A Butterfly Malar Rash Detection Model for Early Systemic Lupus Erythematosus Diagnosis2023 26th International Conference on Computer and Information Technology (ICCIT)10.1109/ICCIT60459.2023.10441593(1-6)Online publication date: 13-Dec-2023
https://doi.org/10.1109/ICCIT60459.2023.10441593

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