Federated Machine Learning for Skin Lesion Diagnosis: An Asynchronous and Weighted Approach

Alsulami, Musleh; Khan, Muhammad Amir; Alsadie, Deafallah; Saudagar, Abdul Khader Jilani; Alkhathami, Mohammed

doi:10.3390/diagnostics13111964

Cited by 11 publications

(6 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The authors in [ 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 ] discussed several elements of skin cancer detection and classification using machine learning. They also looked at the use of deep convolutional neural networks, transfer learning models, infrared thermography, dynamic training and testing augmentation, region-of-interest-based transfer learning, and the combination of human and artificial intelligence in skin cancer detection, classification, and prediction.…”

Section: Related Workmentioning

confidence: 99%

Privacy-Aware Collaborative Learning for Skin Cancer Prediction

Ain,

Khan,

Yaqoob

et al. 2023

Diagnostics

Self Cite

View full text Add to dashboard Cite

Cancer, including the highly dangerous melanoma, is marked by uncontrolled cell growth and the possibility of spreading to other parts of the body. However, the conventional approach to machine learning relies on centralized training data, posing challenges for data privacy in healthcare systems driven by artificial intelligence. The collection of data from diverse sensors leads to increased computing costs, while privacy restrictions make it challenging to employ traditional machine learning methods. Researchers are currently confronted with the formidable task of developing a skin cancer prediction technique that takes privacy concerns into account while simultaneously improving accuracy. In this work, we aimed to propose a decentralized privacy-aware learning mechanism to accurately predict melanoma skin cancer. In this research we analyzed federated learning from the skin cancer database. The results from the study showed that 92% accuracy was achieved by the proposed method, which was higher than baseline algorithms.

show abstract

Section: Related Workmentioning

confidence: 99%

Privacy-Aware Collaborative Learning for Skin Cancer Prediction

Ain,

Khan,

Yaqoob

et al. 2023

Diagnostics

Self Cite

View full text Add to dashboard Cite

show abstract

“…FL provides a privacy-aware machine learning environment for healthcare service providers. Recently, FL-based detection of skin cancer has been proposed [57][58][59][60][61]. Skin cancer detection in a FL-based distributed learning environment is different as compared to traditional machine learning settings.…”

Section: Skin Cancer Detection Using Fl: a Privacy-aware Approachmentioning

confidence: 99%

“…PA maintains performance without adding complexity while preserving privacy and lowering communication costs. An asynchronous weighted aggregation method with CNN for skin lesion prediction is proposed in [61], which asynchronously aggregates the received weights to lower the communication cost. Their suggested method optimizes communication rounds by dividing the CNN layers into shallow and deep layers, with the shallow layers being updated more frequently.…”

Section: Skin Cancer Detection Using Fl: a Privacy-aware Approachmentioning

confidence: 99%

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

et al. 2023

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…For tackling the issues of asynchronous communication and data imbalance in distant healthcare systems, authors in [24][25][26] offer federated machine learning. The authors suggest employing federated learning in an asynchronous and weighted manner to increase the precision of disease detection.…”

Section: Related Workmentioning

confidence: 99%

Asynchronous Federated Learning for Improved Cardiovascular Disease Prediction Using Artificial Intelligence

et al. 2023

Self Cite

View full text Add to dashboard Cite

Healthcare professionals consider predicting heart disease an essential task and deep learning has proven to be a promising approach for achieving this goal. This research paper introduces a novel method called the asynchronous federated deep learning approach for cardiac prediction (AFLCP), which combines a heart disease dataset and deep neural networks (DNNs) with an asynchronous learning technique. The proposed approach employs a method for asynchronously updating the parameters of DNNs and incorporates a temporally weighted aggregation technique to enhance the accuracy and convergence of the central model. To evaluate the effectiveness of the proposed AFLCP method, two datasets with various DNN architectures are tested, and the results demonstrate that the AFLCP approach outperforms the baseline method in terms of both communication cost and model accuracy.

show abstract

Federated Machine Learning for Skin Lesion Diagnosis: An Asynchronous and Weighted Approach

Cited by 11 publications

References 33 publications

Privacy-Aware Collaborative Learning for Skin Cancer Prediction

Privacy-Aware Collaborative Learning for Skin Cancer Prediction

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

Asynchronous Federated Learning for Improved Cardiovascular Disease Prediction Using Artificial Intelligence

Contact Info

Product

Resources

About