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Symmetry
Volume 15
Issue 7
10.3390/sym15071369
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Review

Symmetry in Privacy-Based Healthcare: A Review of Skin Cancer Detection and Classification Using Federated Learning

by
Muhammad Mateen Yaqoob
1,
Musleh Alsulami
2,*,
Muhammad Amir Khan
1,3,
Deafallah Alsadie
2,
Abdul Khader Jilani Saudagar
4,
Mohammed AlKhathami
4 and
Umar Farooq Khattak
5,*
1
Department of Computer Science, COMSATS University Islamabad, Abbottabad Campus, Abbottabad 22060, Pakistan
2
Information Systems Department, Umm Al-Qura University, Makkah 21961, Saudi Arabia
3
Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Shah Alam 40450, Selangor, Malaysia
4
Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia
5
School of Information Technology, UNITAR International University, Kelana Jaya, Petaling Jaya 47301, Selongar, Malaysia
*
Authors to whom correspondence should be addressed.
Symmetry 2023, 15(7), 1369; https://doi.org/10.3390/sym15071369
Submission received: 21 May 2023 / Revised: 13 June 2023 / Accepted: 3 July 2023 / Published: 5 July 2023
(This article belongs to the Special Issue Image Processing and Symmetry: Topics and Applications)
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Abstract

Skin cancer represents one of the most lethal and prevalent types of cancer observed in the human population. When diagnosed in its early stages, melanoma, a form of skin cancer, can be effectively treated and cured. Machine learning algorithms play a crucial role in facilitating the timely detection of skin cancer and aiding in the accurate diagnosis and appropriate treatment of patients. However, the implementation of traditional machine learning approaches for skin disease diagnosis is impeded by privacy regulations, which necessitate centralized processing of patient data in cloud environments. To overcome the challenges associated with data privacy, federated learning emerges as a promising solution, enabling the development of privacy-aware healthcare systems for skin cancer diagnosis. This paper presents a comprehensive review that examines the obstacles faced by conventional machine learning algorithms and explores the integration of federated learning in the context of privacy-conscious skin cancer prediction healthcare systems. It provides discussion on the various datasets available for skin cancer prediction and provides a performance comparison of various machine learning and federated learning techniques for skin lesion prediction. The objective is to highlight the advantages offered by federated learning and its potential for addressing privacy concerns in the realm of skin cancer diagnosis.
Keywords: lightweight network; image processing; privacy-aware machine learning; federated machine learning; skin cancer prediction; melanoma skin cancer; deep learning; healthcare lightweight network; image processing; privacy-aware machine learning; federated machine learning; skin cancer prediction; melanoma skin cancer; deep learning; healthcare

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MDPI and ACS Style

Yaqoob, M.M.; Alsulami, M.; Khan, M.A.; Alsadie, D.; Saudagar, A.K.J.; AlKhathami, M.; Khattak, U.F. Symmetry in Privacy-Based Healthcare: A Review of Skin Cancer Detection and Classification Using Federated Learning. Symmetry 2023, 15, 1369. https://doi.org/10.3390/sym15071369

AMA Style

Yaqoob MM, Alsulami M, Khan MA, Alsadie D, Saudagar AKJ, AlKhathami M, Khattak UF. Symmetry in Privacy-Based Healthcare: A Review of Skin Cancer Detection and Classification Using Federated Learning. Symmetry. 2023; 15(7):1369. https://doi.org/10.3390/sym15071369

Chicago/Turabian Style

Yaqoob, Muhammad Mateen, Musleh Alsulami, Muhammad Amir Khan, Deafallah Alsadie, Abdul Khader Jilani Saudagar, Mohammed AlKhathami, and Umar Farooq Khattak. 2023. "Symmetry in Privacy-Based Healthcare: A Review of Skin Cancer Detection and Classification Using Federated Learning" Symmetry 15, no. 7: 1369. https://doi.org/10.3390/sym15071369

APA Style

Yaqoob, M. M., Alsulami, M., Khan, M. A., Alsadie, D., Saudagar, A. K. J., AlKhathami, M., & Khattak, U. F. (2023). Symmetry in Privacy-Based Healthcare: A Review of Skin Cancer Detection and Classification Using Federated Learning. Symmetry, 15(7), 1369. https://doi.org/10.3390/sym15071369

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