An Adaptive Federated Machine Learning-Based Intelligent System for Skin Disease Detection: A Step toward an Intelligent Dermoscopy Device
Open accessAbstract
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.
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Hashmani et al. [30] applied FL on dermoscopy images to classify nine different skin diseases.
“Moreover, Hashmani et al. [30] applied FL on a series of dermoscopy images to classify nine different skin diseases.”
From A systematic review on federated learning in medical image analysis (2023) · cites this paper as [30]
Similar data has been used in [30], which was released under ISIC 2019 and contains 25,331 dermoscopy images.
“Similar data has been used in [30], which was released under a dataset challenge competition called ISIC 2019 and contains 25,331 dermoscopy images.”
From A systematic review on federated learning in medical image analysis (2023) · cites this paper as [30]
The accuracy scores of [30], [25], and [24] were not clear, but they discussed accuracy between 90-95%.
“Score of [30], [25] and [24] is not clear, they discussed the accuracy between 90-95%.”
From A systematic review on federated learning in medical image analysis (2023) · cites this paper as [30]
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- Authors
- Hashmani, M. A., Jameel, S. M., Rizvi, S. S. H., & Shukla, S.
- Affiliations
- Universiti Teknologi Petronas (MY)
- Shaheed Zulfiqar Ali Bhutto Institute of Science and Technology (PK)
- National University of Ireland (IE)
- Corresponding author
- Jameel, Syed Muslim
- Venue
- Applied Sciences
- Publisher
- MDPI AG
- Published
- 2021-02-28
- Volume
- 11
- Issue
- 5
- Pages
- 2145
- ISSN
- 2076-3417 (ISSN-L)
- OpenAlex
- W3135686383
- Cited by
- 53
- References
- 37
- Citations by year
- 2026: 9 · 2025: 12 · 2024: 11 · 2023: 14 · 2022: 5 · 2021: 2
- FWCI
- 2.91
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Applied Sciences (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Universiti Teknologi PETRONAS (matching grant scheme) (Cost Centre: 015ME0-057)
- Topics
- Health Sciences › Medicine › Oncology › Cutaneous Melanoma Detection and Management
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Keywords
- federated learning, skin disease detection, skin tumor diagnosis, dermoscopy, edge computing
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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