An adaptive federated machine learning-based intelligent system for skin disease detection: A step toward an intelligent dermoscopy device

The prevalence of skin diseases has increased dramatically in recent decades, and they are now considered major chronic diseases globally. People suffer from a broad spectrum of skin diseases, whereas skin tumors are potentially aggressive and life-threatening. However, the severity of skin tumors c...

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Main Authors: Hashmani, M.A., Jameel, S.M., Rizvi, S.S.H., Shukla, S.
Format: Article
Published: MDPI AG 2021
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102700033&doi=10.3390%2fapp11052145&partnerID=40&md5=98f1af80a16ae486ed27393b99701927
http://eprints.utp.edu.my/23933/
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spelling my.utp.eprints.239332021-08-19T13:23:39Z An adaptive federated machine learning-based intelligent system for skin disease detection: A step toward an intelligent dermoscopy device Hashmani, M.A. Jameel, S.M. Rizvi, S.S.H. Shukla, S. The prevalence of skin diseases has increased dramatically in recent decades, and they are now considered major chronic diseases globally. People suffer from a broad spectrum of skin diseases, whereas skin tumors are potentially aggressive and life-threatening. However, the severity of skin tumors can be managed (by treatment) if diagnosed early. Health practitioners usually apply manual or computer vision-based tools for skin tumor diagnosis, which may cause misinterpretation of the disease and lead to a longer analysis time. However, cutting-edge technologies such as deep learning using the federated machine learning approach have enabled health practitioners (dermatologists) in diagnosing the type and severity level of skin diseases. Therefore, this study proposes an adaptive federated machine learning-based skin disease model (using an adaptive ensemble convolutional neural network as the core classifier) in a step toward an intelligent dermoscopy device for dermatologists. The proposed federated machine learning-based architecture consists of intelligent local edges (dermoscopy) and a global point (server). The proposed architecture can diagnose the type of disease and continuously improve its accuracy. Experiments were carried out in a simulated environment using the International Skin Imaging Collaboration (ISIC) 2019 dataset (dermoscopy images) to test and validate the model's classification accuracy and adaptability. In the future, this study may lead to the development of a federated machine learning-based (hardware) dermoscopy device to assist dermatologists in skin tumor diagnosis. © 2021 by the authors. Licensee MDPI, Basel, Switzerland. MDPI AG 2021 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102700033&doi=10.3390%2fapp11052145&partnerID=40&md5=98f1af80a16ae486ed27393b99701927 Hashmani, M.A. and Jameel, S.M. and Rizvi, S.S.H. and Shukla, S. (2021) An adaptive federated machine learning-based intelligent system for skin disease detection: A step toward an intelligent dermoscopy device. Applied Sciences (Switzerland), 11 (5). pp. 1-19. http://eprints.utp.edu.my/23933/
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
description The prevalence of skin diseases has increased dramatically in recent decades, and they are now considered major chronic diseases globally. People suffer from a broad spectrum of skin diseases, whereas skin tumors are potentially aggressive and life-threatening. However, the severity of skin tumors can be managed (by treatment) if diagnosed early. Health practitioners usually apply manual or computer vision-based tools for skin tumor diagnosis, which may cause misinterpretation of the disease and lead to a longer analysis time. However, cutting-edge technologies such as deep learning using the federated machine learning approach have enabled health practitioners (dermatologists) in diagnosing the type and severity level of skin diseases. Therefore, this study proposes an adaptive federated machine learning-based skin disease model (using an adaptive ensemble convolutional neural network as the core classifier) in a step toward an intelligent dermoscopy device for dermatologists. The proposed federated machine learning-based architecture consists of intelligent local edges (dermoscopy) and a global point (server). The proposed architecture can diagnose the type of disease and continuously improve its accuracy. Experiments were carried out in a simulated environment using the International Skin Imaging Collaboration (ISIC) 2019 dataset (dermoscopy images) to test and validate the model's classification accuracy and adaptability. In the future, this study may lead to the development of a federated machine learning-based (hardware) dermoscopy device to assist dermatologists in skin tumor diagnosis. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
format Article
author Hashmani, M.A.
Jameel, S.M.
Rizvi, S.S.H.
Shukla, S.
spellingShingle Hashmani, M.A.
Jameel, S.M.
Rizvi, S.S.H.
Shukla, S.
An adaptive federated machine learning-based intelligent system for skin disease detection: A step toward an intelligent dermoscopy device
author_facet Hashmani, M.A.
Jameel, S.M.
Rizvi, S.S.H.
Shukla, S.
author_sort Hashmani, M.A.
title An adaptive federated machine learning-based intelligent system for skin disease detection: A step toward an intelligent dermoscopy device
title_short An adaptive federated machine learning-based intelligent system for skin disease detection: A step toward an intelligent dermoscopy device
title_full An adaptive federated machine learning-based intelligent system for skin disease detection: A step toward an intelligent dermoscopy device
title_fullStr An adaptive federated machine learning-based intelligent system for skin disease detection: A step toward an intelligent dermoscopy device
title_full_unstemmed An adaptive federated machine learning-based intelligent system for skin disease detection: A step toward an intelligent dermoscopy device
title_sort adaptive federated machine learning-based intelligent system for skin disease detection: a step toward an intelligent dermoscopy device
publisher MDPI AG
publishDate 2021
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102700033&doi=10.3390%2fapp11052145&partnerID=40&md5=98f1af80a16ae486ed27393b99701927
http://eprints.utp.edu.my/23933/
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