eprintid: 15129
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/51/29
datestamp: 2023-11-10 03:29:44
lastmod: 2023-11-10 03:29:44
status_changed: 2023-11-10 01:58:42
type: article
metadata_visibility: show
creators_name: Hashmani, M.A.
creators_name: Jameel, S.M.
creators_name: Rizvi, S.S.H.
creators_name: Shukla, S.
title: An adaptive federated machine learning-based intelligent system for skin disease detection: A step toward an intelligent dermoscopy device
ispublished: pub
note: cited By 15
abstract: 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.
date: 2021
publisher: MDPI AG
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102700033&doi=10.3390%2fapp11052145&partnerID=40&md5=98f1af80a16ae486ed27393b99701927
id_number: 10.3390/app11052145
full_text_status: none
publication: Applied Sciences (Switzerland)
volume: 11
number: 5
pagerange: 1-19
refereed: TRUE
issn: 20763417
citation:   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.  ISSN 20763417