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

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.

Federated Learning in Real-Time Medical IoT: Optimizing Privacy and Accuracy for Chronic Disease Monitoring

“…Federated learning is a promising solution that tackles these problems by implementing a decentralized machine learning approach, where the training process is distributed among multiple edge devices. This shift in paradigm not only maintains data privacy by keeping sensitive information in a local environment, but also improves the precision of predictive models through collaboration and the aggregation of knowledge [6], [7].…”

A brain tumour classification on the magnetic resonance images using convolutional neural network based privacy‐preserving federated learning

“…Furthermore, it has been used in a variety of applications, including medical image classification, 6 disease classification, 7 medical image segmentation 8 and so on. Additional research has been conducted on the effects of FL on a range of other cancers, including breast cancer, 9 prostate cancer, 10 lung cancer, 11 larynx cancer, 12 rectal cancer, 13,14 skin cancer 15 and others.…”