An Adaptive Federated Machine Learning-Based Intelligent System for Skin Disease Detection: A Step toward an Intelligent Dermoscopy Device

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.

A Review of Medical Federated Learning: Applications in Oncology and Cancer Research

“…Additional studies have explored Federated Learning on a variety of other cancers, including less common types. Some of the types covered in the uses cases we reviewed included: skin cancer [42,43], breast cancer [44,45], prostate cancer [46], lung cancer [47], pancreatic cancer, anal cancer, and thyroid cancer. [42] used the ISIC 2018 dataset [48] to simulate a Federated Learning environment for classifying skin lesions.…”

Deterioration of Electrical Load Forecasting Models in a Smart Grid Environment

“…Among the literature survey, Jameel S. et al [49][50][51] proposed multiple adaptive frameworks in their studies. They presented an adaptive framework for different ML and DL applications that included complex and multispectral image analysis, image classification following the digital transformation of IoT and IR 4.0, and disease identification in skins to detect it at an early stage.…”

“…These models are capable of self-learning and adaptation to changes. Though the researchers are covering some dynamics in other fields for the transformation of models in classification applications [49][50][51], some propositions in regression works also exist [25,40]. Still, in regression, many features need to be incorporated when the task is specifically of electrical load forecasting to enable the model to be eligible for multi-modality or Smart Grid.…”

Skin Disease Detection: Machine Learning vs Deep Learning

“…This approach consists of intelligent local edges (dermoscopy) and a global point (server). This architecture can able to diagnosis the skin type, skin diseases type and also improve the accuracy constantly [8].…”